CN117460474A

CN117460474A - Tissue treatment energy delivery using optical imaging

Info

Publication number: CN117460474A
Application number: CN202280026537.8A
Authority: CN
Inventors: 凯斯特·胡利安·巴彻勒; 泰奥·亨·吉米·扬; 小长武子; 乔丹·戈隆布
Original assignee: Gyrus ACMI Inc
Current assignee: Gyrus ACMI Inc
Priority date: 2021-03-02
Filing date: 2022-03-02
Publication date: 2024-01-26

Abstract

A system for imaging and treating tissue may include or use: an imaging sensor adapted to receive imaging information from a location inside a human or animal subject; a tissue treatment output device for applying tissue treatment to tissue at a location inside the subject; and a controller circuit comprising a signal processing circuit configured to: the imaging information is image processed to determine a structure or other characteristic at or near a location within the subject based at least in part on the imaging information, and to customize parameters or algorithms for controlling the tissue treatment output device.

Description

Tissue treatment energy delivery using optical imaging

Priority claim

The present application claims priority to each of U.S. provisional application serial No. 63/155,444 filed on 3/2 of 2021, U.S. provisional application serial No. 63/271,053 filed on 22 of 2021, and U.S. provisional application serial No. 63/265,978 filed on 12/23 of 2021, the entire disclosures of each of which are incorporated herein by reference in their entireties, and claims the benefit of priority to each of the above applications.

Background

Various different types of energy, such as Radio Frequency (RF) or other electromagnetic, plasma or ultrasonic energy, may be used for vessel sealing, tissue cutting or cauterization, tissue ablation or tissue coagulation, etc., alone or in combination with mechanical energy delivery (e.g., using a sharp cutter) or manipulation (e.g., using forceps). There are many types of monopolar and bipolar energy devices used for different surgical purposes. In one example of an energy delivery device ("energy device"), forceps may be used, for example, for laparoscopic surgery. Forceps may be used to control fine motion, for example, within a patient, and may include a grasping assembly and/or a cutting assembly. In addition, the pliers can also utilize electrical energy in the grasping assembly. The electrosurgical occlusion forceps may further include or use energy devices such as RF, ultrasound and microwave vascular occlusion devices. The forceps may grip the tissue and the elastin or collagen of the gripped tissue may be melted by the energy device, e.g., may seal the tissue.

Disclosure of Invention

The inventors have recognized that, in addition, tissue treatment procedures using energy delivery may benefit from improvements including shortening the procedure time, reducing treatment complications, and reducing the level of skill necessary for the surgeon or other practitioner performing the procedure, for example, to help reduce or avoid the need for surgical "skill" or techniques to accommodate changing conditions at the target site during the procedure. For example, a method of electrosurgical may include or use an electrosurgical device having a Radio Frequency (RF) or other electromagnetic energy delivery system with near instantaneous feedback regarding one or more conditions at a target site (e.g., tissue impedance, phase angle of therapeutic power delivery, etc.) to adjust electrosurgical therapeutic energy delivered to tissue at the target site based on an electrical feedback signal representative of one or more such conditions at the target site. However, a single generic RF electromagnetic energy waveform application process may be applied to different tissue types-for example, during a particular procedure, such as during vascular occlusion, or during monopolar or bipolar cutting, or during any other tissue treatment, a single generic RF electromagnetic energy waveform application process may exhibit different characteristics. For example, carotid arteries and renal arteries have different vessel occlusion characteristics, but in one approach, each of such vessels may be controlled by the same therapeutic energy waveform application process. Similar considerations may also exist for other tissues, such as tissues that may be more specifically treated, such as fat, ligaments, or other tissues. Thus, in some cases, feedback from electrical tissue characteristics (e.g., tissue impedance) from the target tissue site alone may be insufficient to properly adjust energy therapy delivery, or may be insufficient to properly differentiate tissue types, allowing for customization, adjustment, or other nuances in the manner in which electrical energy is delivered to the target tissue site. For example, the inventors have recognized that, in addition, carotid arteries should have relatively slow applied electrotherapy power, e.g., for vessel occlusion, while renal arteries can withstand faster power applications without creating tissue "pop" problems. This tissue burst is the following phenomenon: the vapors generated within the tissue are, for example, expelled from between the jaws of the forceps, applying mechanical pressure and electrical energy together at a rate that may cause damage to the vessel wall, including at one or more locations that may be remote from the target vessel sealing site. Thus, feedback other than or different from electrical feedback may be used to help make different, e.g., better tailored, energy delivery decisions.

The electrosurgical method may also help manage the target closing force provided by the end effector regardless of the amount of tissue along the jaw contact area. For example, during electrosurgical treatment, the electrosurgical system may assist the user in achieving a desired tissue pressure between the jaws. The system may determine the target closing force based on the visual feedback. In an example, the system may determine a target tissue pressure based on the vessel type, and then determine a target closing force required to provide the target tissue pressure to the vessel. In another example, the target tissue pressure may be estimated based on a measured tissue parameter (e.g., vessel diameter), and the system may then determine a target closing force required to provide the target tissue pressure to the tissue. The system may include or use a processing unit configured to establish, adjust or modulate the jaw closing force or energy waveform based on data collected from the sensors. The processing unit may use data collected from the sensors to help determine a target tissue pressure or a target closing force.

Drawings

Fig. 1A shows a side view of an example of an electrosurgical system.

Fig. 1B shows an example of a jaw assembly in a first position.

Fig. 1C shows an example of a jaw assembly in a second position.

Fig. 1D shows an example of an electrosurgical system compressing a target object.

FIG. 1E illustrates an example of an integrated camera device for use with an end effector assembly.

Fig. 2 shows an example of a surgeon using an electrosurgical system.

FIG. 3 illustrates a block diagram of an example of a machine on which one or more methods discussed herein may be implemented.

Fig. 4 is a flow chart of a method of using an example of an electrosurgical system including a jaw electrosurgical device.

Fig. 5 is a flow chart of an example method of using a universal electrosurgical system.

Detailed Description

The present disclosure relates to devices and methods for treating tissue such as, but not limited to, blood vessels. In methods for tissue treatment, energy devices such as Radio Frequency (RF), ultrasound, and microwave vascular occlusion devices may be used as part of electrosurgical occlusion forceps. An end effector assembly, such as may include forceps, may provide the required clamping pressure to force tissue for electrosurgical sealing. The energy device may be used, for example, to apply energy to treat one or more tissue components, such as collagen or elastin of tissue, and the heated or melted components may be used, for example, to seal the tissue. Some electrosurgical sealing forceps may also include a cutting element as part of the end effector, such as a fixed or movable cutting blade for cutting a sealed vessel. Such devices may use relatively high clamping forces to compress vascular tissue for occlusion. However, this method has the following problems: if the tissue is not held at the proper target tissue pressure, unwanted jaw closing forces may cause unnecessary tissue damage during surgery. For example, if the area between the jaws is not filled with tissue, providing an appropriate target tissue pressure can be challenging for the user. In this case, the closing force required by the end effector to provide the target tissue pressure may vary based on the amount of tissue in contact with the contact surface area of the jaws. For example, where the amount of tissue clamped under a fixed closing force is relatively small, the tissue pressure provided is greater than if the amount of tissue clamped under the same fixed closing force is relatively large. Furthermore, providing the desired tissue pressure can be challenging for a user due to the different tissue thicknesses, morphologies, sizes, volumes, densities, compositions, moisture content, and the like. In general, a "target tissue pressure" may refer to a relatively small range of pressures that are acceptable in surgery, and providing the target tissue pressure is important, for example, for a user to successfully perform a surgery. If the closing force applied is too small, the tissue will be compressed at too small a tissue, and the resulting closure will develop its "burst pressure" (e.g., the blood pressure required to prevent the formed closure from rupturing) may be very low. This is because the "gums" formed by the melted elements of the vessel, such as collagen, are not sufficiently pressed together. Such insufficient gum pressure can result in weak pressure treated bond interfaces and poor closure. If the closing force applied is too great, tissue pressure is too great, irreparable tissue damage, such as tearing or ripping of tissue, may occur. In addition, if the closing force applied is too great and the tissue pressure is too great, tissue pop may also occur if the steam generated in the tissue is trapped between the jaws under high pressure while energy is applied until the jaws are released after surgery. At this time, the steam in the vessel wall escapes at a high speed, and damages surrounding tissues. This may lead to post-occlusion weaknesses which may later rupture during recovery of the patient from anesthesia or post-operation (failure at the site of the weakened end of the "occluded" vessel due to the patient's elevated blood pressure to a specific blood pressure level within the vessel).

The inventors have recognized that, in addition, creating a tissue treatment system that is capable of determining and providing a "target" closing force provided by an end effector can help a user achieve a target tissue pressure between jaws during energy delivery treatment (e.g., for vascular occlusion), regardless of the amount of tissue along the contact area of the jaws. The target closing force may be determined and established or adjusted by the system, e.g., based on visual feedback, e.g., using tissue images generated by an imaging sensor (e.g., an imaging device). In an example, the system may use information from the image to help determine a target tissue pressure based on, for example, the identified tissue type (e.g., vessel type), and then determine a target closure force suitable to provide the target tissue pressure to the vessel or other tissue. In another example, the target tissue pressure may be estimated or calculated based on one or more measured tissue parameters, such as, but not limited to, a vessel cross-sectional dimension, such as a vessel diameter, and the system may then determine a target closing force required to provide the target tissue pressure to the tissue. Since the vessel is natural and its cross-section does not conform to an exact circle, the vessel diameter may include an approximate diameter, a measured diameter, an average diameter, a minimum diameter, a maximum diameter, or a calculated diameter obtained by one or more measurements. The one or more tissue parameters may additionally or alternatively correspond to one or more of the following tissue characteristics: thickness, morphology, size, volume, density, composition, moisture content, dynamic modulus, viscoelasticity, conductivity, impedance, color or non-visible light reflection, scattering or other optically responsive feedback, or other describable properties. Alternatively or additionally, the system may use a tissue parameter to establish, adjust, or modulate another parameter of the electrosurgical system, such as the amplitude, frequency, pulse width, current, phase angle, or other electrical characteristic of the electrosurgical energy signal. The system may include or use a processing unit configured to establish, adjust, limit, or modulate one or both of the jaw closing force or energy waveform, for example, based on data collected from imaging and/or other sensors. The processing unit 122 may use data collected from the sensors, for example, to determine a target tissue pressure or a target closing force. Customizing one or both of the force or electrical characteristics applied by the device to one or more tissue parameters may be beneficial because it may improve the performance of the device in different tissues.

One or more visualization or optical imaging techniques may be used, for example, to identify a particular tissue or tissue type, for example, within a visualization region. Such techniques may include using image signal processing, such as with a trained learning model trained, for example, using one or more machine learning or other Artificial Intelligence (AI) techniques, to, for example, help identify one or more specific tissues, tissue types, or tissue structures, etc. at or near a target site or scene (e.g., within an endoscopic, laparoscopic, or other patient internal visualization region). Specific tissue may also be identified at the patient external visualization area. For example, such visualization or optical imaging may be configured to identify a particular anatomy or tissue or feature thereof using the visualization or optical imaging information. When tissue is positioned or located (e.g., relative to an end effector) for vascular occlusion or similar treatment, such as by electrotherapy or other therapeutic medical devices, particular treatments or treatment parameter settings (e.g., procedures, waveforms, etc.) may be selected. The particular treatment or treatment parameter setting may be selected from a variety of available treatment or treatment parameter settings, for example, may be customized based at least in part on the use of visual or optical imaging information. Visual or optical imaging information may also be used in combination with other information, such as electrical information (e.g., tissue impedance), as described herein. In examples, when a tissue type or characteristic cannot be determined (e.g., a desired accuracy cannot be achieved) using visualization or optical imaging techniques, a particular "trade-off" vessel occlusion (or other treatment) treatment or treatment waveform or parameter may be selected, e.g., based at least in part on suitability for all or a subset of the tissue type, or based at least in part on electrical tissue characteristics, e.g., tissue impedance characteristics, phase angle of treatment power delivery, etc.

Fig. 1A, 1B, 1C, 1D, and 1E illustrate examples of portions of an electrosurgical system 100. The electrosurgical system 100 may include or use a medical device 102 having an end effector assembly 104. As described herein, the system 100 may be configured to provide customized energy outputs to various devices. In the illustrated example, the system 100 can provide a customized waveform to one or more electrodes, such as the active electrode 111 (electrode 111 depicted in fig. 1D). Such customized energy output may be used to treat tissue, such as for sealing, cutting, ablating, electrocautery, or desiccation effects. In some examples, the end effector assembly 104 includes both an active electrode and a return electrode, however, the device need not be forceps or bipolar, as shown in the example shown. The waveforms delivered by the processing unit 122 may also be tailored for monopolar or other types of devices. For example, the active electrode of the device may be used in conjunction with a remote return electrode (e.g., without limitation, a return electrode pad).

Further, the customized energy outputs described herein may be used in systems that deliver any type of energy output compatible with a particular end effector or device. In several non-limiting examples provided for clarity, the custom energy output may include: the ultrasound energy or radio frequency energy delivered to/by the ultrasound forceps or the thermal energy delivered to/by the forceps or other device is tailored for treatment such as cutting, sealing, coagulation, ablation, drying, electrocautery, etc.

End effector assembly 104 may include or use jaw assembly 106. Alternatively or additionally, the end effector assembly 104 may include or use a "J-hook" type electrode or other electrode type for surgery. Jaw assembly 106 may include a first jaw member 108 and a second jaw member 110. Second jaw member 110 may be pivotally coupled to first jaw member 108 about a pivot or other pivot point 112. One or more drivers 119 may be included or used in the device 102, with the one or more drivers 119 being housed, for example, in the handle 118 and mechanically coupled to the end effector assembly 104. Further, one or more drivers 119 may also be included at or near the distal end of the assembly 104, such as to directly drive the jaw assembly 106. The driver 119 may be any suitable driver associated with or coupled to the end effector in any arrangement, including robotic applications and the like.

The electrosurgical system 100 may also include or use one or more onboard or independent sensors 114 for determining tissue characteristics. For example, as shown in fig. 1A, the sensor 114 may be separate from the end effector assembly, such as extending from another shaft or support of the device 102, or such as extending from another device (e.g., a robotic arm or video mirror). In addition, the sensor 114 may also be integrated at or near the end effector assembly 104. The types of force sensors may include, for example, strain, piezoelectric, inductive, capacitive, magnetostrictive, hydraulic, or pneumatic, which may measure forces such as compression, strain, contact, displacement, bending, stretching, shearing, or torque. Such force sensors 114 (or electrical sensors 114) may be located in the end effector 104, e.g., in components of one or more of the jaw members 108, 110 or tissue sealing plate 109, which tissue sealing plate 109 may include an active electrode 111 and a return electrode 113 in this example. The force sensor 114 may be located at any other suitable location on the forceps for measuring the force or displacement of the jaws 108, 110. In an example, the sensor 114 (e.g., camera device) may be located at or near the distal end of the end effector assembly, on the jaws of the jaw assembly 106, or integrated with the end effector assembly 104 (e.g., J-hook) (as shown in fig. 1E).

As shown in fig. 1B and 1C, jaws 108 and 110 of jaw assembly 106 may be movable between a first position a in which first jaw member 108 and second jaw member 110 are spaced apart from each other, and a second position B in which first jaw member 108 and second jaw member 110 are positioned closer to each other than in first position a. As shown in fig. 1D, the end effector assembly 104 may compress a blood vessel or other target object 124, such as a portion of the body, anatomical feature, tissue, vein, artery, or combination thereof of a human or animal subject. In an example, the end effector assembly 104 may be used in the system 100, for example, to compress one or more of lymphatic vessels, tissue pedicles, arteries, and veins, e.g., having a diameter or similar cross-sectional dimension in the range from about 0.5mm to about 7 mm. In this context, the diameter of a vessel may refer to the measured diameter or the average diameter along the relevant length of the relevant vessel. In another example, the end effector assembly 104 may be used with the system 100, for example, to compress lymphatic or arterial vessels having a diameter or other similar cross-sectional dimension greater than 7 mm. At least one of the first jaw member 108 and the second jaw member 110 may include an electrode that may be adapted to be electrically connected to an electrosurgical energy source, for example, for providing an electrical current that may pass through the electrode of the end effector assembly 104. For example, when tissue is located within the jaws, a therapeutic current may be passed from the first jaw member 108 to the second jaw member 110, and the therapeutic current may coagulate blood, cauterize, cut, or a combination thereof. The end effector assembly 104 may generally include one or more working assemblies, such as pairs of jaws, and sufficient control for operating one or more working assemblies. The end effector assembly 104 may include components for performing the described functions, and may include an elongated or other shaft 116 (e.g., a tubular member, a hollow tube, or an assembly of tubes), a handpiece 118, one or more operable mechanisms (e.g., an actuator 120) for manipulating the shaft 116 or actuating the end effector assembly 104, or a combination thereof. The handpiece 118 may be an assembly capable of forming a component or housing structure of the handpiece 118 structure having a cavity. In an example, the shaft 116 and end effector assembly 104 may be included on or mounted to an end of a robotic arm, allowing the robot to stabilize, position, maneuver, and operate, rather than being held by a user grasping the handpiece 118.

In an example, the sensor 114 may include an imaging device capable of illuminating and capturing images of tissue, such as an imaging sensor pixel array using a Focal Plane Array (FPA). For example, as shown in fig. 1E, the sensor 114 may be a camera device located at the distal end of the end effector assembly 104. One or more cameras 114 may be integrated at or near the jaw assembly 106 or J-hook (as shown). One or more light sources 115 may be included in the system 100, such as near the camera 114, for example, for illumination of a surgical site. When the camera 114 is integrated into the device 102, manipulation of the device may locate the camera 114 simultaneously or concurrently. The tissue image may include one or more device-identifiable features of the tissue, such as by applying a trained learning model or other image signal processing to the image to identify one or more features in the image. Sensor 114 may also include electrical sensors or electrodes, for example, for providing an electrical characterization of tissue compressed between first jaw member 108 and second jaw member 110 during or after application of electrosurgical treatment energy to the tissue or in-terleap with application of electrosurgical treatment energy. Further, the sensor 114 may be accompanied by a fiber optic bundle or other illumination optics to, for example, deliver light from an external light source to an internal target, and may include one or more of the following: spectral imaging or analytical sensors, hyperspectral imaging sensors, colorimetric imaging sensors, video camera sensors, infrared imaging sensors, ultrasound imaging sensors, 3D imaging sensors, LIDAR imaging sensors, optical Coherence Tomography (OCT) imaging sensors, focal Plane Array (FPA) imaging sensors, or fluorescence or other offset wavelength responsive imaging sensors. The sensor 114 may include a force sensor, such as embedded or otherwise included in the jaw assembly 106, for measuring displacement of the clamping force of tissue. The force sensors included in sensor 114 may include, for example, strain gauges, load cells, piezoresistive sensors, inductive sensors, capacitive sensors, or magnetostrictive sensors. The sensor 114 may include an electrical sensor for measuring one or more electrical characteristics (e.g., an electrical characteristic of tissue, such as resistance, capacitance, or inductance).

The sensor 114 may be communicatively coupled to the processing unit 122. Processing unit 122 may receive data from sensors 114 and may determine a compression force parameter based on the data, such as determining a target clamping force of jaw assembly 106 using the data. In an example, processing unit 122 may generate a tissue image of tissue compressed between first jaw member 108 and second jaw member 110. The tissue image may include one or more device-identifiable features of the tissue. The processing unit 122 may be configured to identify or identify various types or characteristics of tissue or vessels (e.g., carotid, femoral, renal, pulmonary, or other types of particular arteries, veins, or lymphatic vessels), for example. In an example, the processing unit 122 may use a trained learning model, an algorithmic signal processing method, or both, for example, to identify or identify one or more device-identifiable features of the tissue image, such as one or more shapes, patterns, colors. In an example, these features may be cross-referenced to a database of image feature data. The processing unit 122 may also manipulate the image, the feature, or both, e.g., change from color to gray or monochrome, or segment or delineate certain features, e.g., to help improve feature recognition. In an example, the processing unit 122 may include or use depth of view analysis circuitry, for example, to help obtain a representation of 3D features of tissue.

Further, the processing unit 122 may include or use or be capable of executing a machine learning model that is trained to identify or identify various types of vessels using the sample images. The processing unit 122 may include or use one or more algorithms capable of extracting data from the sample image (e.g., data for algorithmic analysis of the sample image by compression, filtering, edge detection, corner detection, spot detection, ridge detection, hough transform, image segmentation, optical flow, genetic Algorithm (GA), or other techniques). One or more algorithms may be capable of digital image processing, such as extracting relevant data from a sample image to enable identification of relevant vascular features. The machine learning model may include or use training data received as input, such as training data classified from a human user. The model may include or use one or more prediction engines. The prediction engine may include or use several engine parameters such as data sources, algorithms, configuration inputs, or other characteristics of the engine under consideration. The prediction engine may include data source parameters, such as symptom data entered by a user. The model may include a plurality of algorithms and source code to assist in algorithmically deriving labels representing correctly identified vessels by generating prediction engine variants using training data. The model may replace the prediction engine variant with a new prediction engine variant based on training data received as input, the performance of the prediction engine variant, or both. The prediction engine variables may be selected by an operator (e.g., a human user) or may be automatically generated. The selection of engine parameters may be marked or replayed by the prediction engine, for example, to evaluate and adjust the prediction engine. In some examples, an operator may manually determine one or more new engine variants, e.g., to troubleshoot, adjust, or otherwise reload the prediction engine. The prediction engine may locally use or include training data, such as training data specifically received with respect to multiple sample images generated by a single user. Alternatively or additionally, the prediction engine may globally use or include training data, e.g., collectively receive collective training data regarding sample images generated by multiple users. The prediction engine may interact with one or more servers, which may be capable of data storage, local data communication, global data communication, or any combination thereof. The prediction engine may interact with a website, for example, for global data communications.

In some implementations, the machine learning model may be an artificial neural network. Artificial neural networks are artificial in that they are computational entities inspired by biological neural networks, but modified for implementation by computing devices. Artificial neural networks are used to model complex relationships between inputs and outputs, or to find patterns of data in cases where dependencies between inputs and outputs are not easily determinable. Neural networks typically include an input layer, one or more intermediate ("hidden") layers, and an output layer, each layer including several nodes. The number of nodes at different levels may be different. A neural network is considered "deep" when it includes two or more hidden layers. The nodes in each layer are connected to some or all of the nodes in the subsequent layers, and the weights of these connections are typically learned from the data during the training process, for example by back propagation (backpropagation) in which the network parameters are adjusted to produce the desired output given the corresponding input in the labeled training data. Thus, the artificial neural network is an adaptive system 100, which adaptive system 100 is configured to change its structure (e.g., connection configuration and/or weights) based on information flowing through the network during training, and the weights of the hidden layers can be regarded as coding of meaningful patterns in the data.

The fully connected neural network is the following: each node in the input layer is connected to each node in the subsequent layer (the first hidden layer), each node in the first hidden layer is connected to each node in the subsequent hidden layer in turn, and so on until each node in the last hidden layer is connected to each node in the output layer.

In an example, the machine learning model may include or use a Convolutional Neural Network (CNN). CNN is an artificial neural network, and as with the artificial neural network described above, CNN is composed of nodes and has a weight that can be learned. However, a layer of CNNs may have nodes arranged in three dimensions: width, height, and depth correspond to a 2 x 2 array of pixel values (e.g., width and height) in each video frame and the number of video frames (e.g., depth) in the sequence, respectively. A node of a layer may be only locally connected to a small area of its previous width and height layer, called the acceptance domain. The hidden layer weights may take the form of convolution filters applied to the acceptance domain. In some examples, the convolution filter may be two-dimensional, so the same filter may be reused for each frame (or convolution transform of an image) or a specified subset of frames in the input volume (volume). In other examples, the convolution filter may be three-dimensional and thus may extend to the entire node depth of the input volume. The nodes in each convolution layer of the CNN may share weights such that the convolution filter of a given layer may replicate across the entire width and height of the input volume (e.g., the entire frame), thereby reducing the overall number of trainable weights and improving the applicability of the CNN to data sets other than training data. The values of the layers may be pooled to reduce the number of computations in subsequent layers (e.g., values representing certain pixels may be passed forward while others are discarded), and any discarded values may also be reintroduced along the depth of the CNN pool mask to restore the number of data points to the previous size. Multiple layers (optionally, some of which are fully connected) may be stacked to form a CNN architecture. The machine learning model may also be at least one of a Support Vector Machine (SVM), K-nearest neighbor (KNN), artificial Neural Network (ANN), or a collective model combining SVM and ANN.

In an example, processing unit 122 may use data from sensor 114 to measure or estimate a diameter of a vessel sensed by the sensor (e.g., a vessel in line of sight of or in contact with the sensor, a vessel located between first jaw member 108 and second jaw member 110, or a vessel compressed between first jaw member 108 and second jaw member 110). For example, the processing unit 122 may use device identifiable features from the sample image, e.g., to measure or estimate the diameter of the vessel. Further, the pivot angle of the first jaw member 108 and the second jaw member 110 may be measured and used, for example, to help estimate the diameter of the vessel. Further, processing unit 122 may use data from sensors 114 to measure or estimate the amount of tissue compressed between the jaws. Processing unit 122 may use data from sensor 114 to determine the contact area of tissue compressed between first jaw member 108 or second jaw member 110. As described herein, the vessel diameter or other cross-sectional external or internal dimensions may include an approximate diameter, a measured diameter, an average diameter, a minimum diameter, a maximum diameter, or a calculated diameter obtained by one or more measurements.

The processing unit 122 may determine the target tissue pressure based on, for example, the identified vessel type, the identified vessel location, or the identified vessel diameter. The processing unit 122 may determine the target closing force based on, for example, an amount of tissue sensed by the sensor, an amount of tissue located between the jaws, an amount of tissue compressed between the jaws, a contact area of tissue compressed between the jaws, the identified vessel type and the identified vessel position or the identified vessel diameter. The processing unit 122 may assist in delivering the determined target closing force to the end effector assembly 104. The end effector may receive input from the processing unit 122, for example, to apply a target closing force through actuation of one or more drivers (e.g., motors, micro-motors, etc.). In another example, the robotic unit may receive input from the processing unit 122, for example, to apply a target closing force. In an example, the target closing force or target tissue pressure may be determined by the processing unit 122 and may serve as a threshold input for user feedback. For example, the user of the end effector assembly 104 may receive visual, audible, tactile, or other feedback from the processing unit 122 when the determined target closing force or the determined target tissue pressure is reached or exceeded. Further, the user may receive visual or audible or tactile or other feedback when approaching the determined target closing force or the determined target tissue pressure.

In another example, the processing unit 122 may determine a target tissue pressure or a target closing force and use either to establish, adjust, or modulate the energy waveform, for example, to help compensate for, for example, excessive tissue pressure or closing force. Alternatively or additionally to adjusting the target clamping force, the magnitude of the electrosurgical energy may also be established or adjusted, for example to mitigate side effects of the treatment on the patient, such as overheating of tissue or "pop". For example, the electrosurgical system 100 may be configured to identify a particular anatomy or tissue or feature thereof using visual or optical imaging information. When tissue is positioned or located for vascular occlusion or similar treatment, such as by electrotherapy or other therapeutic medical devices, a particular treatment or treatment parameter setting (e.g., procedure, waveform, etc.) may be selected, such as from a variety of available treatments or treatment parameter settings, e.g., may be customized based at least in part on the use of visualization or optical imaging information, which may also be used in combination with other information, such as electrical information (e.g., tissue impedance). In examples, when a tissue type or characteristic cannot be determined (e.g., a desired accuracy cannot be achieved) using visualization or optical imaging techniques, a particular "trade-off" vessel occlusion (or other treatment) treatment or treatment waveform or parameter may be selected, e.g., based at least in part on suitability for all or multiple tissue types, or based at least in part on electrical tissue characteristics, e.g., tissue impedance characteristics, phase angle of treatment power delivery, etc. In the event that the processing unit 122 is unable to adequately determine the tissue type or characteristics, an alert may be output to notify the user of such an event; informing the user that less customized output is being provided; or alert the user to reposition the end effector relative to the tissue, such as for additional sensing.

Feedback or other sensed input signals based on visual or optical imaging information may be used as a "predictive" selection of therapeutic energy delivery waveforms, therapeutic or therapeutic parameters, which selection may optionally be further combined with one or more other feedback or other sensed information, e.g., based on tissue impedance, energy delivery phase angle, or other characteristics, which may be monitored during tissue reconstruction or other therapeutic delivery and used as feedback signals for signal processing and control adjustment or timing or change of therapeutic delivery.

Additionally or alternatively, the waveform or other treatment parameter or characteristic or treatment may be selected based at least in part on information derived from the visualization or optical imaging rather than specific information of the target site to be treated. For example, if the target tissue to be treated is near a risky Organ (OAR) or heat sensitive region (e.g., colon), a waveform or other treatment parameter or characteristic or treatment may be selected or adjusted based at least in part on such information, e.g., to help create as little thermal margin as possible, or within a thermal budget that is specific to such circumstances. The presence or proximity of a risky organ or other thermally sensitive region may be determined using visual or optical imaging information, for example, one or more image processing techniques (e.g., segmentation, atlas, trained statistical learning model), and the internal imaging information from the first sensor may be combined with external video or still camera information from the second sensor, for example, to provide an indication of patient position, orientation, or movement, which may be image processed, for example, to help determine proximity to one or more such other anatomical regions of interest, for therapy delivery or to avoid therapy. The visual or optical imaging information may alternatively or additionally be used to detect one or more structures that should not be altered by the treatment, for example to automatically stop the application of treatment energy (or provide a user alert) before reaching a thermal margin or limit of one or more such structures that are not altered by the delivery of treatment energy. In the event that therapeutic energy delivery is affected by one or more other components of the system 100 (e.g., electrotherapy using forceps to squeeze a closed vessel during occlusion), one or more control parameters or other parameters (e.g., grasping force) associated with these other components may be at least one of specified, adjusted, alerted (e.g., alerted to a user using acoustic, tactile, or visual feedback), restricted, or controlled (e.g., using robotic forceps controller circuitry) based at least in part on these visual or optical imaging information.

For example, different tissues behave differently under compression (e.g., by forceps during vessel occlusion). In some examples, the vessel is compressed to a lateral dimension that is less than 40% of its static dimension when not compressed, with no additional benefit to thermal margin or burst pressure, while other tissues require a greater amount of compression to reduce thermal margin.

In addition, vessel occlusion or other electrotherapy waveforms may be selected based on other considerations derived from an understanding of the visualization or optical images. Some areas, such as tissue adjacent to another organ, such as the colon, may benefit from treatment with as little thermal margin as possible during the activation and closure process. But the thermal margin may be a tradeoff for activation speed. A slower activation rate may be used to reduce or minimize the thermal margin, for example, to provide only a small amount of energy before the start of the vapor condensation period of the cycle. Such treatment timing, speed, and energy considerations may also be controlled, for example, based at least in part on the visual or optical imaging information.

There are many different tissue materials that a user may wish to activate and cut. These tissues may include fat, mesentery, ligaments, and the like. These tissues may be activated using a different therapeutic output regimen than that used for vascular occlusion. Such different treatment output protocols may similarly be selected, established, or adapted based at least in part on the visual or optical imaging information, such as using imaging processing and trained machine learning or trained statistical models to identify treatment-targeted or non-targeted tissue types or characteristics prior to or during treatment delivery, including in combination with one or more other factors, such as tissue or treatment-delivered electrical characteristics (e.g., tissue impedance or treatment power phase angle).

Thus, the present technique is not limited to use in vascular closure devices, but may be applied elsewhere, including for monopolar and bipolar cutting devices, such as devices intended to hemostatically cut tissue. For example, the present techniques may be used not only to visually discover vessels at a tissue surface, but also to use optical imaging techniques (e.g., involving absorption, scattering, etc.), for example, to help identify vessels or other structures of such tissue under a visually observable surface.

For example, when a surgeon or other user is cutting different sections of tissue material having not only different fat levels but also vascularity or differences in vascularity, the appropriate custom treatment settings may be selected prior to initiating tissue treatment. This will advantageously help react faster during treatment (controlled by the user or by an automated treatment system using feedback signals during treatment (e.g., such as tissue impedance, power delivery phase angle, etc.) than would otherwise be the case for a particular condition (e.g., massive hemorrhage).

Furthermore, the present technique may also be used to detect structures that should not be altered or affected by the treatment and to help stop the application of energy, for example, when the device is brought to a thermal margin distance from the structure that is to be protected from the treatment or from excessive effects. For example, during gynecological procedures, the ureter may be damaged, for example, involving the application of electrical or other energy to nearby tissue. However, the present techniques may include modifying the output energy when the visual or optical imaging information indicates that there is a structure in the vicinity that needs protection, such as shutting down energy delivery, alerting the user, or requesting user confirmation to override such system control.

Thus, the present techniques may include "predictive" or a priori energy delivery choices (e.g., selection or custom algorithms or simpler waveforms) that may be combined with one or more feedback signals (e.g., tissue resistance, tissue reaction, tissue impedance, phase angle, etc.) used during tissue reconstruction. The technology can be used for electric energy treatment, monopole, bipolar, RF, ultrasonic, laser, microwave, resistor and the like.

The present system may include or be coupled to an endoscope or other visualization device, for example, may provide illumination, photodetectors, or a multi-pixel imaging array to a tissue site within a patient, for example, to convert responsive light from the tissue site within the patient for optical imaging. The system may also include an electrical signal processing circuit. This may include imaging processing circuitry, for example, which may be used to process the converted signals into an electronic representation of the optical image. The represented images may be used by signal processing circuitry, for example, for training a neural network or other machine learning or statistical learning model (or used with training), such as those described herein. In this way, image recognition techniques may be used to help identify tissue type, tissue material, tissue characteristics, or tissue structure. This visual or optical imaging information, in turn, may be used to tailor one or more parameters of treatment delivery, for example to better treat a region of interest, or to help avoid affecting one or more other sensitive nearby regions or structures. Such a priori visual or optical imaging information may be used in combination with persistent information obtained during treatment delivery (e.g., information about tissue characteristics or treatment characteristics) to further tailor, limit, or control treatment delivery. Further, examples herein may also include providing the user of the end effector assembly 104 with the ability to receive visual, tactile, or audible feedback from the processing unit 122, for example, in view of the particular sensed tissue type or tissue parameter observed within or grasped by the end effector, for example, to help instruct or suggest the operator to stop or shorten the energy application period, or to inform the user that too much tissue pressure or closing force is being applied, based on the amount of tissue pressure or closing force, for example, after energy application and appropriate tissue pressure or closing force.

The processing unit 122 may include or use processing circuitry. The processing unit 122 is not limited to a single unit or circuit. The processing unit 122 may include multiple processing sources, and some of the processing sources may be physically separate from the device or remotely located. In an example, the processing unit 122 may include or use memory circuitry including instructions that, when executed by the processing circuitry of the processing unit 122, may cause the processing unit 122 to perform inspection operations using the sensor 114. The processing circuitry may include or use image processing circuitry configured to determine a contact area of tissue compressed between first jaw member 108 and second jaw member 110, for example, using a tissue image and a trained machine learning model. The processing circuitry may include or use image processing circuitry configured to determine a contact area of tissue compressed between first jaw member 108 and second jaw member 110 using the tissue image and an automated algorithm. The processing circuitry may include or use image processing circuitry configured to determine a cross-sectional diameter of at least one vessel contained in tissue compressed between first jaw member 108 and second jaw member 110 using the tissue image and the trained machine learning model. The processing circuitry may include or use image processing circuitry configured to determine a cross-sectional diameter of at least one vessel contained in tissue compressed between first jaw member 108 and second jaw member 110 using the tissue image and an automated algorithm.

Fig. 2 shows an example of an electrosurgical system for use by a surgeon. The system may include or use one or more sensors 114, the sensors 114 configured to examine tissue compressed between the first jaw member 108 and the second jaw member 110. Sensor 114 may also examine tissue at any location within the line of sight of the sensor, adjacent to end effector assembly 104, or at a location between first jaw member 108 and second jaw member 110, either before or after tissue is compressed by end effector assembly 104 or in the event that tissue is ultimately not compressed. For example, the tissue image may be a pre-compression image that the processing unit 122 uses to help determine a target clamping force for the jaw assembly. Further, the tissue image may be an image of the compressed tissue between first jaw member 108 and second jaw member 110, and may be used by processing unit 122 to help determine, modulate, or adjust parameters of the jaw assembly, such as a target clamping force applied to the compressed tissue.

In addition, one or more sensors 114 may measure mechanical or electrical characteristics of tissue to help establish, adjust, or modulate parameters of the jaw assembly. For example, the electrosurgical system may include or use a spectrum or ionization circuit that may be used, for example, to determine the composition of tissue. Optical sensing circuitry may also be used, for example at one or more IR wavelengths or with dyes or fluorescence, to aid the sensor 114 in visualizing tissue.

As previously described, the sensor 114 may be a camera device capable of capturing an image of tissue, and the tissue image may include device-identifiable features of the tissue. For example, the camera device may provide real-time, online, or other concurrent video monitoring feeds to the surgeon, and the frames of the feeds may be used to capture device-identifiable features of the tissue. During surgery, the video monitoring feed may be viewed on the display 126. The video monitoring feed may include or use one or more visual or tactile or audible indicators, for example, to provide feedback to the surgeon during surgery. The indicator may provide feedback to the surgeon, for example, to assist the surgeon in manually applying the target clamping force using the end effector assembly 104. Additionally or alternatively, the processing unit 122 may also use device-identifiable features to facilitate automatic application of the target clamping force using the end effector assembly 104.

In an example, data from the sensor 114 may be used to close the evaluation circuit, for example, to ensure a desired closure of the tissue after treatment. For example, data from sensor 114 (e.g., oximeter data or vascular color data) may be used, for example, to indicate whether hemoglobin is present in the tissue. In an example, the closure evaluation circuit may include or use a processor for automatically viewing one or more characteristics corresponding to a desired closure, the characteristics being determined from the data. For example, if a desired tissue characteristic is not detected after a treatment procedure, the system may alert the surgeon or operator if the characteristic is determined. In addition, if one or more undesirable tissue characteristics are detected, the system may alert the surgeon or operator after the treatment procedure.

Fig. 3 illustrates a block diagram of an example of a machine 300 on which one or more of the methods as discussed herein may be implemented. In one or more examples, one electrosurgical system 100 may be implemented by a machine 300. In alternative examples, machine 300 operates as a standalone device or may be connected (e.g., networked) to other machines. In one or more examples, the electrosurgical system 100 may include one or more items of the machine 300. In a networked deployment, the machine 300 may operate in the capacity of a server or a client machine in server-client network environment, or as a peer machine in a peer-to-peer (or distributed) network environment. The machine may be a Personal Computer (PC), a tablet PC, a set-top box (STB), a Personal Digital Assistant (PDA), a cellular telephone, a network appliance, a network router, switch or bridge, or any machine capable of executing instructions (sequential or otherwise) that specify actions to be taken by that machine. Furthermore, while only a single machine is illustrated, the term "machine" shall also be taken to include any collection of machines that individually or jointly execute a set (or multiple sets) of instructions to perform any one or more of the methodologies discussed herein.

The example machine 300 includes a processor 302 (e.g., CPU, GPU, ASIC, circuitry (e.g., one or more transistors, resistors, capacitors, inductors, diodes, logic gates, multiplexers, buffers, modulators, demodulators, radios (e.g., transmitting or receiving radio or transceiver)), a sensor 321 (e.g., a transducer that converts one form of energy (e.g., light, heat, electricity, mechanical or other energy) to another form of energy), etc., or a combination thereof), a main memory 304, and a static memory 306, which communicate with each other via a bus 308. The machine 300 (e.g., a computer system) may also include a video display unit 310 (e.g., a Liquid Crystal Display (LCD) or a Cathode Ray Tube (CRT)). The machine 300 also includes an alphanumeric input device 312 (e.g., a keyboard), a User Interface (UI) navigation device 314 (e.g., a mouse), a disk drive or mass storage unit 316, a signal generation device 318 (e.g., a speaker), and a network interface device 320.

The disk drive or mass storage unit 316 includes a machine-readable medium 322 on which is stored one or more sets of instructions and data structures (e.g., software) 324 embodying or utilized by any one or more of the methodologies or functions described herein. During execution of the instructions 324 by the machine 300, the instructions may also reside, completely or at least partially, within the main memory 304 and/or within the processor 302, the main memory 304 and the processor 302 also constituting machine-readable media.

The machine 300 as shown includes an output controller 328. The output controller 328 manages the flow of data to and from the machine 300. The output controller 328 is sometimes referred to as a device controller, wherein the software that directly interacts with the output controller 328 is referred to as a device driver.

While the machine-readable medium 322 is shown in an example to be a single medium, the term "machine-readable medium" may include a single medium or multiple media (e.g., a centralized or distributed database, and/or associated caches and servers) that store the one or more instructions or data structures. The term "machine-readable medium" shall also be taken to include any tangible medium that is capable of storing, encoding or carrying instructions for execution by the machine and that cause the machine to perform any one or more of the methodologies of the present disclosure, or that is capable of storing, encoding or carrying data structures utilized by or associated with such instructions. The term "machine-readable medium" shall accordingly be taken to include, but not be limited to, solid-state memories, and optical and magnetic media. Specific examples of machine-readable media include nonvolatile memory including, by way of example: semiconductor memory devices such as erasable programmable read-only memory (EPROM), EEPROM, and flash memory devices; magnetic disks, such as internal hard disks and removable disks; magneto-optical disk; CD-ROM discs and DVD-ROM discs.

The instructions 324 may also be transmitted or received over a communications network 326 using a transmission medium. The instructions 324 may be transmitted using the network interface device 320 and any of a number of well-known transport protocols (e.g., HTTP). Examples of communication networks include LANs, WANs, the internet, mobile telephone networks, plain Old Telephone (POTS) networks, and wireless data networks (e.g., wiFi and WiMax networks). The term "transmission medium" shall be taken to include any intangible medium that is capable of storing, encoding or carrying instructions for execution by the machine, and includes digital or analog communications signals or other intangible medium to facilitate communication of such software.

Fig. 4 is a flow chart of a method of using an example of an electrosurgical system including a jaw electrosurgical device. In an example, the method of treating tissue 400 may be performed using one of several electrosurgical systems described herein. At 402, between a first position and a second position, a first jaw member and a second jaw member of an end effector assembly of a medical device may be articulated. At 404, tissue may be forced between the first jaw member and the second jaw member of the end effector assembly. At 406, at least one of the first jaw member and the second jaw member may be energized using an electrosurgical energy source. At 408, the pressed tissue may be examined using the sensor. At 410, image data from the sensor can be used to determine a target clamping force of the end effector assembly.

Fig. 5 is a flow chart of a method of using an example of a generic electrosurgical system. In an example, the method of treating tissue 500 may be performed using one of several electrosurgical systems as described herein. At 502, imaging information may be received, for example, via an imaging sensor, from a location inside a human or animal subject. Imaging information may also be received from a location external to the human or animal subject. At 504, a structure or other characteristic at or near a location inside or outside the subject may be determined based on the imaging information. And at 506, parameters or algorithms, such as parameters or algorithms for controlling the tissue treatment output device, may be customized based at least in part on the imaging information. And at 508, tissue treatment may be applied to tissue at a location internal or external to the subject, for example, via a tissue treatment output device. For example, the customization parameters or algorithms may include: the target clamping force of the jaw assembly of the end effector is customized, e.g., for sealing, cauterizing, or cutting, based at least in part on the imaging information. In addition, customizing parameters or algorithms may also include customizing the energy waveform based at least in part on imaging information, such as for occlusion, cauterization, or cutting. For example, the customization parameters or algorithms may include or use customizing the electrosurgical energy signal based at least in part on the imaging information. In some examples, customizing the energy waveform may include: tailoring the energy waveform to any type of energy-based tissue treatment also includes means for ablating, drying, slicing or electrocautery. The custom energy waveform is not limited to forceps-type devices with either a clamping or bipolar function. For example, tailoring the energy waveform based on the imaging information may be applied to any electrosurgical device, including any other bipolar or monopolar device, such as, but not limited to, a spatula, an electrotome (bovie), a scalpel, and the like.

The systems described herein may customize the output delivered to the treatment device based on one or more inputs received from one or more sensors (e.g., image information from an image sensor, force information from a force sensor, electrical information from an electrical sensor), and may customize one or more of the following: an output related to energy; and an output related to the force. In the illustrated example provided, the energy output is shown as radiofrequency energy delivered to the electrodes of the forceps, but may also include ultrasound, microwave, electromagnetic, resistive, thermal, laser energy in the forceps or other type of treatment device. In the example shown, the force may be an applied mechanism including compression, displacement, pressure, such as those that occur upon closure of the jaws of the pliers by movement of a user in a hand-operated device or by actuation in a robotic device.

Annotation and examples

The following non-limiting examples detail certain aspects of the inventive subject matter to address challenges and provide benefits and the like discussed herein.

Aspect 1 is a system for imaging and treating tissue, the system comprising: an imaging sensor adapted to receive imaging information from a location inside a human or animal subject; a tissue treatment output device for applying tissue treatment to tissue at a location inside the subject; and a controller circuit comprising a signal processing circuit configured to: the imaging information is image processed to determine a structure or other characteristic at or near a location inside the subject based at least in part on the imaging information, and to customize parameters or algorithms for controlling the tissue treatment output device.

In aspect 2, the subject matter of aspect 1, wherein the imaging sensor comprises an imaging device.

In aspect 3, the subject matter of aspect 2, wherein the controller circuitry is configured to generate a tissue image of the tissue at a location inside the subject.

In aspect 4, the subject matter of any one of aspects 1 to 3, comprising an end effector comprising the tissue treatment output device.

In aspect 5, the subject matter of aspect 4, wherein the end effector comprises a forceps comprising a jaw assembly comprising: a first jaw member; and a second jaw member pivotably coupled to the first jaw member, wherein: the jaw assembly is movable between a first position in which the first and second jaw members are spaced apart from one another and a second position in which the first and second jaw members are positioned closer to one another than in the first position; and at least one of the first jaw member and the second jaw member includes the tissue treatment output device.

In aspect 6, the subject matter of aspect 5, wherein the parameter comprises a target clamping force of the jaw assembly; and the controller circuit is configured to establish or adjust the target clamping force using the imaging information.

In aspect 7, the subject matter of any one of aspects 5 to 6, wherein the controller circuitry is configured to perform at least one of the following based at least in part on the imaging information: providing a control signal to the end effector assembly; or provide a user alert to a user controlling the end effector assembly.

In aspect 8, the subject matter of any one of aspects 5 to 7, wherein the controller circuit is configured to: identifying one or more features from a tissue image of tissue compressed between the first jaw member and the second jaw member; and the one or more identified characteristics of the tissue include a width of tissue compressed between the first and second jaw members, the width of tissue being a measure of tissue contacting the jaw members between respective proximal and distal portions of the jaw members.

In aspect 9, the subject matter of any one of aspects 5 to 8, wherein the controller circuit is configured to: identifying one or more features from a tissue image of the tissue compressed between the first and second jaw members; and the one or more identified characteristics of tissue include an indication of an amount of tissue compressed between the first jaw member and the second jaw member.

In aspect 10, the subject matter of any one of aspects 5 to 9, wherein the controller circuit is configured to: identifying one or more features from a tissue image of tissue compressed between the first jaw member and the second jaw member; and the one or more identified characteristics of the tissue include an indication of an approximate diameter of a vessel compressed between the first and second jaw members.

In aspect 11, the subject matter of any of aspects 5-10, wherein the controller circuit includes an image processing circuit configured to use the tissue image to determine a contact area of the tissue compressed between the first jaw member and the second jaw member.

In aspect 12, the subject matter of any one of aspects 1 to 11, wherein the parameter comprises an energy characteristic of the tissue treatment; and the controller circuit is configured to modulate an energy characteristic of the tissue treatment using the imaging information.

In aspect 13, the subject matter of any one of aspects 1-12, wherein the controller circuit is configured to modulate the parameter modulation, including electrosurgical energy signals, based at least in part on the imaging information, the imaging information including imaging information from a treatment target structure location.

In aspect 14, the subject matter of any one of aspects 1 to 13, wherein the controller circuit is configured to: identifying one or more features from the imaging information, the imaging information including a tissue image of the tissue at a location inside the subject; and establishing or adjusting an electrosurgical energy signal provided by the tissue treatment output device based at least in part on one or more features identified from imaging information comprising a tissue image of the tissue.

Aspect 15 is a system for treating tissue, the system comprising: an imaging sensor adapted to receive imaging information from a location inside a human or animal subject; a tissue treatment output device for applying tissue treatment to tissue at a location inside the subject; and a controller circuit comprising a signal processing circuit configured to: the imaging information is image processed to determine a structure or other characteristic at or near a location inside the subject based at least in part on the imaging information, and to customize tissue treatment compression force parameters for controlling the tissue treatment output device.

In aspect 16, the subject matter of aspect 15, wherein the imaging sensor includes an imaging device.

In aspect 17, the subject matter of any one of aspects 15-16, comprising an end effector comprising a jaw assembly, wherein the compression force parameter comprises a target clamping force of the jaw assembly.

Aspect 18 is a method for treating tissue, comprising: receiving imaging information from a location inside a human or animal subject via an imaging sensor; determining a structure or other characteristic at or near a location inside the subject based on the imaging information; customizing a parameter or algorithm based at least in part on the imaging information, the parameter or algorithm controlling a tissue treatment output device; and applying tissue treatment to tissue at a location inside the subject via a tissue treatment output device.

In aspect 19, the subject matter of aspect 18, wherein the imaging sensor comprises an imaging device.

In aspect 20, the subject matter of any of aspects 18 to 19, wherein customizing parameters or algorithms comprises: a target clamping force of a jaw assembly of the end effector is customized for at least one of sealing, cauterizing, or cutting based at least in part on the imaging information.

In aspect 21, the subject matter of any of aspects 18 to 20, wherein customizing parameters or algorithms comprises: an electrosurgical energy signal is customized based at least in part on the imaging information.

Aspect 22 is a system, comprising: a visualization or optical imaging component adapted to receive visualization or optical imaging information from a location inside a human or animal subject; a tissue treatment output device for applying energy to tissue at a location inside the subject; and a controller circuit comprising a signal processing circuit configured to: image processing the visual or optical imaging information to train or use a trained model for image recognition to determine structures or other characteristics at or near a location inside the subject based at least in part on the visual or optical imaging information, and customizing parameters or algorithms for controlling the tissue treatment output device.

In aspect 23, the subject matter of aspect 22, further comprising an electrical tissue property sensor arranged to sense an electrical property of tissue at or near a location inside the subject, and wherein the controller circuit is configured to control the tissue treatment output device based at least in part on information about the sensed electrical property.

In aspect 24, the subject matter of any one of aspects 22-23, wherein the tissue treatment output device further comprises a mechanical device for assisting in applying the energy to the tissue at a location internal to the subject, and wherein the controller circuit is further configured to perform at least one of the following based at least in part on the visual or optical imaging information: providing a control signal to the mechanical device; or provide a user alert to a user controlling the mechanical device.

In aspect 25, the subject matter of any one of aspects 22 to 24, wherein the controller circuitry is configured to control the tissue treatment output device based at least in part on visual or optical imaging information from a treatment target structure location.

In aspect 26, the subject matter of any of aspects 22-25, wherein the controller circuitry is configured to control the tissue treatment output device based at least in part on visual or optical imaging information from a treatment avoidance structure location.

Aspect 27 is a method of controlling therapy delivery or providing user alerts using the system of aspect 22.

Aspect 28 is an apparatus-readable medium comprising instructions for performing the method of aspect 27.

Aspect 29 is an apparatus-readable medium comprising a trained neural network, machine learning, or other model for performing the method of aspect 27, wherein the model uses the visualization or optical imaging information.

Aspect 30 is a system for treating tissue, comprising: an end effector assembly of a medical device, the end effector assembly comprising a jaw assembly comprising: a first jaw member; and a second jaw member pivotally coupled to the first jaw member, wherein the jaw assembly is movable between a first position in which the first and second jaw members are spaced apart from one another and a second position in which the first and second jaw members are positioned closer to one another; and at least one of the first and second jaw members is adapted to be connected to a source of electrosurgical energy; a sensor configured to examine tissue; and a processing unit configured to receive data from the sensor and to use the data to modulate a parameter of the jaw assembly.

In aspect 31, the subject matter of aspect 30, wherein the parameter of the jaw assembly is a target clamping force.

In aspect 32, the subject matter of any one of aspects 30-31, wherein the parameter of the jaw assembly is a frequency or current of electrosurgical energy from the source of electrosurgical energy.

In aspect 33, the subject matter of any one of aspects 30-32, wherein the tissue is compressed between the first jaw member and the second jaw member for examination therein.

In aspect 34, the subject matter of any one of aspects 30 to 33, wherein the sensor is an imaging device.

In aspect 35, the subject matter of aspect 34, wherein the processing unit is configured to generate a tissue image of the tissue compressed between the first jaw member and the second jaw member, the tissue image including a device-identifiable feature of the tissue.

In aspect 36, the subject matter of aspect 35, wherein the device identifiable feature of tissue includes an amount of tissue compressed between the first jaw member and the second jaw member.

In aspect 37, the subject matter of any one of aspects 35-36, wherein the device identifiable feature of tissue includes a width of tissue compressed between the first and second jaw members, the width of tissue being a measure of tissue contacting the jaw members between respective proximal and distal ends of the jaw members.

In aspect 38, the subject matter of any of aspects 35-37, wherein the device identifiable feature of tissue comprises an approximate diameter of a vessel compressed between the first jaw member and the second jaw member.

In aspect 39, the subject matter of any of aspects 35-38, wherein the processing unit includes a memory circuit including instructions that, when executed by at least one processor circuit, cause the processing unit to perform a checking operation using the sensor.

In aspect 40, the subject matter of aspect 39, wherein the at least one processor circuit includes an image processing circuit configured to determine a contact area of the tissue compressed between the first jaw member and the second jaw member using the tissue image and a trained machine learning model.

In aspect 41, the subject matter of any of aspects 39-40, wherein the at least one processor circuit includes an image processing circuit configured to determine a contact area of the tissue compressed between the first jaw member and the second jaw member using the tissue image and an automated algorithm.

In aspect 42, the subject matter of any one of aspects 39-41, wherein the at least one processor circuit includes an image processing circuit configured to determine a cross-sectional diameter of at least one vessel contained in the tissue compressed between the first jaw members using the tissue image and a trained machine learning model.

In aspect 43, the subject matter of any one of aspects 39-42, wherein the at least one processor circuit includes an image processing circuit configured to determine a cross-sectional diameter of at least one vessel contained in the tissue compressed between the first jaw members using the tissue image and an automated algorithm.

Aspect 44 is a system for treating tissue, comprising: an end effector assembly of a medical device, the end effector assembly comprising a jaw assembly comprising: a first jaw member; and a second jaw member pivotally coupled to the first jaw member, wherein the jaw assembly is movable between a first position in which the first and second jaw members are spaced apart from one another and a second position in which the first and second jaw members are positioned closer to one another; and at least one of the first and second jaw members is adapted to be connected to a source of electrosurgical energy; a sensor configured to examine tissue compressed between the first jaw member and the second jaw member; and a processing unit configured to receive image data from the sensor and to use the data to determine a target clamping force of the jaw assembly.

Aspect 45 is a method for treating tissue, comprising: articulating the end effector assembly of the medical device between: a first position in which the first and second jaw members of the assembly are spaced apart from one another; and a second position in which the first and second jaw members are positioned closer to one another; forcing the tissue between a first jaw member and a second jaw member of the end effector assembly; energizing at least one of the first jaw member and the second jaw member using an electrosurgical energy source; examining the pressed tissue using a sensor; and determining a target clamping force of the end effector assembly using image data from the sensor.

In aspect 46, the subject matter of aspect 45, wherein the sensor is an imaging device.

In aspect 47, the subject matter of aspect 46, wherein determining the target clamping force of the end effector assembly using the image data from the sensor comprises: a tissue image is generated that includes device identifiable characteristics of the tissue.

In aspect 48, the subject matter of aspect 47, wherein the device identifiable feature of tissue comprises an amount of tissue compressed between the first jaw member and the second jaw member.

In aspect 49, the subject matter of aspect 48, wherein the device identifiable feature of tissue includes a width of tissue compressed between the first jaw member and the second jaw member, the width of tissue being a measure of tissue contacting the jaw members between respective proximal ends and respective distal ends of the jaw members.

Aspect 50 is at least one machine readable medium comprising instructions that when executed by processing circuitry cause the processing circuitry to perform operations to implement any one of aspects 1 to 49.

Aspect 51 is an apparatus comprising means for implementing any one of aspects 1 to 49.

Aspect 52 is a system implementing any one of aspects 1 to 49.

Aspect 53 is a method of implementing any one of aspects 1 to 49.

The above detailed description includes references to the accompanying drawings, which form a part hereof. By way of illustration, the drawings show specific embodiments in which the invention may be practiced. These embodiments are also referred to herein as "examples". Such examples may include elements other than those shown or described. However, the inventors also contemplate examples in which only the elements shown or described are provided. Moreover, the inventors contemplate examples using any combination or permutation of these elements (or one or more aspects of these elements) shown or described with respect to a particular example (or one or more aspects of a particular example) or with respect to other examples (or one or more aspects of other examples) shown or described herein.

In the event of a discrepancy in usage between the present document and any document incorporated by reference, the usage in the present document controls. In this document, the terms "include" and "in … …" are used as concise Chinese equivalents to the respective terms "include" and "wherein. In addition, in the appended claims, the terms "including" and "comprising" are open-ended, i.e., a system, apparatus, article, composition, formulation, or process that includes elements other than those listed after such term in the claims is still considered to fall within the scope of the claims.

In this document, the terms "a" or "an" are used to include one or more than one, regardless of any other instances or usages of "at least one" or "one or more," as is common in patent documents. In this document, the term "or" is used to refer to a non-exclusive or, such that "a or B" includes "a but not B", "B but not a" and "a and B", unless indicated otherwise. In this document, the terms "include" and "in … …" are used as concise Chinese equivalents to the respective terms "include" and "wherein. In addition, in the appended claims, the terms "including" and "comprising" are open-ended, i.e., a system, apparatus, article, composition, formulation, or process that includes elements other than those listed after such term in the claims is still considered to fall within the scope of the claims. Furthermore, in the appended claims, the terms "first," "second," and "third," etc. are used merely as labels, and are not intended to impose numerical requirements on their objects.

The above description is intended to be illustrative and not restrictive. For example, the above-described examples (or one or more aspects of the above-described examples) may be used in combination with each other. After viewing the above description, one of ordinary skill in the art, for example, may use other embodiments. The Abstract is provided to enable the reader to quickly ascertain the nature of the technical disclosure. This abstract is submitted with the understanding that it will not be used to interpret or limit the scope or meaning of the claims. In addition, in the above detailed description, various features may be grouped together to simplify the present disclosure. This should not be interpreted as intending that an unclaimed disclosed feature is essential to any claim. Rather, inventive subject matter may lie in less than all features of a particular disclosed embodiment. Thus, the appended claims are hereby incorporated into the detailed description as examples or embodiments, with each claim standing on its own as a separate embodiment, and it is contemplated that such embodiments may be combined with each other in various combinations or permutations. The scope of the invention should be determined with reference to the appended claims, along with the full scope of equivalents to which such claims are entitled.

Claims

1. A system for imaging and treating tissue, the system comprising:

an imaging sensor adapted to receive imaging information from a location inside a human or animal subject;

a tissue treatment output device for applying tissue treatment to tissue at a location inside the subject; and

a controller circuit comprising a signal processing circuit configured to: the imaging information is image processed to determine a structure or other characteristic at or near a location inside the subject based at least in part on the imaging information, and to customize parameters or algorithms for controlling the tissue treatment output device.

2. The system of claim 1, wherein the imaging sensor comprises a camera device.

3. The system of claim 2, wherein the controller circuit is configured to generate a tissue image of the tissue at a location inside the subject.

4. A system according to any one of claims 1 to 3, comprising an end effector comprising the tissue treatment output device.

5. The system of claim 4, wherein the end effector comprises a forceps comprising a jaw assembly comprising:

A first jaw member; and

a second jaw member pivotally coupled to the first jaw member, wherein,

the jaw assembly is movable between a first position in which the first and second jaw members are spaced apart from one another and a second position in which the first and second jaw members are positioned closer to one another than in the first position; and is also provided with

At least one of the first jaw member and the second jaw member includes the tissue treatment output device.

6. The system of claim 5, wherein,

the parameter includes a target clamping force of the jaw assembly; and is also provided with

The controller circuit is configured to establish or adjust the target clamping force using the imaging information.

7. The system of any of claims 5 to 6, wherein the controller circuit is configured to perform at least one of the following based at least in part on the imaging information: providing a control signal to the end effector assembly; or provide a user alert to a user controlling the end effector assembly.

8. The system of any of claims 5 to 7, wherein the controller circuit is configured to:

Identifying one or more features from a tissue image of the tissue compressed between the first and second jaw members; and

the one or more identified characteristics of the tissue include a width of tissue compressed between the first and second jaw members, the width of tissue being a measure of tissue contacting the jaw members between respective proximal and distal portions of the jaw members.

9. The system of any of claims 5 to 8, wherein the controller circuit is configured to:

the identified one or more characteristics of the tissue include an indication of an amount of tissue compressed between the first and second jaw members.

10. The system of any of claims 5 to 9, wherein the controller circuit is configured to:

The identified one or more characteristics of the tissue include an indication of an approximate diameter of a vessel compressed between the first and second jaw members.

11. The system of any one of claims 5 to 10, wherein the controller circuit comprises an image processing circuit configured to use the tissue image to determine a contact area of the tissue compressed between the first and second jaw members.

12. The system according to any one of claims 1 to 11, wherein,

the parameters include energy characteristics of the tissue treatment; and is also provided with

The controller circuit is configured to modulate an energy characteristic of the tissue treatment using the imaging information.

13. The system of any of claims 1 to 12, wherein the controller circuit is configured to modulate the parameter based at least in part on the imaging information, including modulating an electrosurgical energy signal, the imaging information including imaging information from a treatment target structure location.

14. The system of any one of claims 1 to 13, wherein the controller circuit is configured to:

Identifying one or more features from the imaging information, the imaging information including a tissue image of the tissue at a location inside the subject; and

an electrosurgical energy signal provided by the tissue treatment output device is established or adjusted based at least in part on one or more features identified from imaging information comprising a tissue image of the tissue.

15. A system for treating tissue, the system comprising:

a controller circuit comprising a signal processing circuit configured to: the imaging information is image processed to determine a structure or other characteristic at or near a location inside the subject based at least in part on the imaging information, and to customize tissue treatment compression force parameters for controlling the tissue treatment output device.

16. The system of claim 15, wherein the imaging sensor comprises a camera device.

17. The system of any one of claims 15 to 16, comprising an end effector comprising a jaw assembly, wherein the compression force parameter comprises a target clamping force of the jaw assembly.

18. A system, comprising:

a visualization or optical imaging component adapted to receive visualization or optical imaging information from a location inside a human or animal subject;

a tissue treatment output device for applying energy to tissue at a location inside the subject; and

a controller circuit comprising a signal processing circuit configured to: image processing the visual or optical imaging information to train or use a trained model for image recognition to determine structures or other characteristics at or near a location inside the subject based at least in part on the visual or optical imaging information, and customizing parameters or algorithms for controlling the tissue treatment output device.

19. The system of claim 18, further comprising an electrical tissue property sensor arranged to sense an electrical property of tissue at or near a location inside the subject, and wherein the controller circuit is configured to control the tissue treatment output device based at least in part on information about the sensed electrical property.

20. The system of any of claims 18 to 19, wherein the tissue treatment output device further comprises a mechanical device for assisting in applying the energy to the tissue at a location inside the subject, and wherein the controller circuit is further configured to perform at least one of the following based at least in part on the visualization or optical imaging information: providing a control signal to the mechanical device; or provide a user alert to a user controlling the mechanical device.

21. The system of any one of claims 18 to 20, wherein the controller circuit is configured to control the tissue treatment output device based at least in part on visual or optical imaging information from a treatment target structure location.

22. The system of any one of claims 18 to 21, wherein the controller circuit is configured to control the tissue treatment output device based at least in part on visual or optical imaging information from a treatment avoidance structure location.

23. A method of controlling therapy delivery or providing a user alert using the system of claim 18.

24. An apparatus readable medium comprising instructions for performing the method of claim 23.

25. An apparatus-readable medium comprising a trained neural network, machine learning, or other model for performing the method of claim 23, wherein the model uses the visualization or optical imaging information.

26. A system for treating tissue, comprising:

an end effector assembly of a medical device, the end effector assembly comprising a jaw assembly comprising:

a first jaw member; and

a second jaw member pivotally coupled to the first jaw member, wherein,

the jaw assembly is movable between a first position in which the first and second jaw members are spaced apart from one another and a second position in which the first and second jaw members are positioned closer to one another; and is also provided with

At least one of the first and second jaw members is adapted to be connected to a source of electrosurgical energy;

a sensor configured to examine tissue; and

a processing unit configured to receive data from the sensor and to use the data to modulate parameters of the jaw assembly.

27. The system of claim 26, wherein the parameter of the jaw assembly is a target clamping force.

28. The system of any one of claims 26 to 27, wherein the parameter of the jaw assembly is a frequency or current of electrosurgical energy from the source of electrosurgical energy.

29. The system of any one of claims 26 to 28, wherein the tissue is forced between the first and second jaw members for examination therein.

30. The system of any one of claims 26 to 29, wherein the sensor is a camera device.

31. The system of claim 30, wherein the processing unit is configured to generate a tissue image of the tissue compressed between the first and second jaw members, the tissue image including a device-identifiable feature of the tissue.

32. The system of claim 31, wherein the device identifiable feature of tissue comprises an amount of tissue compressed between the first and second jaw members.

33. The system of any one of claims 31 to 32, wherein the device identifiable feature of tissue comprises a width of tissue compressed between the first and second jaw members, the width of tissue being a measure of tissue contacting the jaw members between their respective proximal and distal ends.

34. The system of any one of claims 31 to 33, wherein the device identifiable feature of tissue comprises an approximate diameter of a vessel compressed between the first and second jaw members.

35. The system of any of claims 31-34, wherein the processing unit comprises a memory circuit comprising instructions that, when executed by at least one processor circuit, cause the processing unit to perform inspection operations using the sensor.

36. The system of claim 35, wherein the at least one processor circuit comprises an image processing circuit configured to determine a contact area of the tissue compressed between the first and second jaw members using the tissue image and a trained machine learning model.

37. The system of any one of claims 35 to 36, wherein the at least one processor circuit comprises an image processing circuit configured to determine a contact area of the tissue compressed between the first and second jaw members using the tissue image and an automated algorithm.

38. The system of any one of claims 35 to 37, wherein the at least one processor circuit includes an image processing circuit configured to determine a cross-sectional diameter of at least one vessel contained in the tissue compressed between the first jaw members using the tissue image and a trained machine learning model.

39. The system of any one of claims 35 to 38, wherein the at least one processor circuit comprises an image processing circuit configured to determine a cross-sectional diameter of at least one vessel contained in the tissue compressed between the first jaw members using the tissue image and an automated algorithm.

40. A system for treating tissue, comprising:

a first jaw member; and

a second jaw member pivotally coupled to the first jaw member, wherein,

a sensor configured to examine tissue compressed between the first jaw member and the second jaw member; and

a processing unit configured to receive image data from the sensor and to use the data to determine a target clamping force of the jaw assembly.

41. A method for treating tissue, comprising:

receiving imaging information from a location inside a human or animal subject via an imaging sensor;

determining a structure or other characteristic at or near a location inside the subject based on the imaging information;

customizing a parameter or algorithm based at least in part on the imaging information, the parameter or algorithm controlling a tissue treatment output device; and

applying tissue treatment to tissue at a location inside the subject via a tissue treatment output device.

42. The method of claim 41, wherein the imaging sensor comprises a camera.

43. The method of any one of claims 41 to 42, wherein customizing parameters or algorithms comprises: a target clamping force of a jaw assembly of the end effector is customized for at least one of sealing, cauterizing, or cutting based at least in part on the imaging information.

44. The method of any one of claims 41 to 43, wherein customizing parameters or algorithms comprises: an electrosurgical energy signal is customized based at least in part on the imaging information.

45. A method for treating tissue, comprising:

articulating the end effector assembly of the medical device between:

a first position in which the first and second jaw members of the assembly are spaced apart from one another; and

a second position in which the first and second jaw members are positioned closer to one another;

forcing the tissue between a first jaw member and a second jaw member of the end effector assembly;

energizing at least one of the first jaw member and the second jaw member using an electrosurgical energy source;

examining the pressed tissue using a sensor; and

the image data from the sensor is used to determine a target clamping force of the end effector assembly.

46. The method of claim 45, wherein the sensor is an imaging device.

47. The method of claim 46, wherein determining a target clamping force of the end effector assembly using the image data from the sensor comprises: a tissue image is generated that includes device identifiable characteristics of the tissue.

48. The method of claim 47, wherein the device identifiable feature of tissue comprises an amount of tissue compressed between the first jaw member and the second jaw member.

49. The method of claim 48, wherein the device identifiable feature of tissue comprises a width of tissue compressed between the first and second jaw members, the width of tissue being a measure of tissue contacting the jaw members between their respective proximal and distal ends.