WO2023098806A1 - 手术等级确定方法、装置、系统、设备及介质 - Google Patents

手术等级确定方法、装置、系统、设备及介质 Download PDF

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WO2023098806A1
WO2023098806A1 PCT/CN2022/135879 CN2022135879W WO2023098806A1 WO 2023098806 A1 WO2023098806 A1 WO 2023098806A1 CN 2022135879 W CN2022135879 W CN 2022135879W WO 2023098806 A1 WO2023098806 A1 WO 2023098806A1
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automation level
surgical
level
data
functional
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PCT/CN2022/135879
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English (en)
French (fr)
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任梓为
江磊
苗燕楠
梁玄清
张明
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上海微创医疗机器人（集团）股份有限公司
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Publication of WO2023098806A1 publication Critical patent/WO2023098806A1/zh

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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V10/00—Arrangements for image or video recognition or understanding
    • G06V10/70—Arrangements for image or video recognition or understanding using pattern recognition or machine learning
    • G06V10/764—Arrangements for image or video recognition or understanding using pattern recognition or machine learning using classification, e.g. of video objects
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H40/00—ICT specially adapted for the management or administration of healthcare resources or facilities; ICT specially adapted for the management or operation of medical equipment or devices
    • G16H40/60—ICT specially adapted for the management or administration of healthcare resources or facilities; ICT specially adapted for the management or operation of medical equipment or devices for the operation of medical equipment or devices
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H50/00—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
    • G16H50/30—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for calculating health indices; for individual health risk assessment

Definitions

  • the present application relates to the technical field of medical devices, in particular to a method, device, system, equipment and medium for determining a surgical grade.
  • Surgical robots are designed to precisely perform complex surgical procedures in a minimally invasive manner.
  • Surgical robots have been developed in the face of various limitations in traditional surgical operations.
  • Surgical robots have broken through the limitations of the human eye. They can use stereoscopic imaging technology to present the internal organs of the human body to the operator more clearly. And for the narrow areas where some people's hands cannot reach, the surgical robot can still control the surgical instruments to complete the movement, swing, clamping and 360° rotation, and can avoid shaking, improve the accuracy of surgery, and further achieve small wounds and less bleeding , fast postoperative recovery, and greatly shorten the postoperative hospital stay of the surgical object. Therefore, surgical robots are favored by doctors and patients, and are widely used in their respective clinical operations.
  • the purpose of the present application is to provide a method, device, system, equipment and medium for determining the level of surgery, so as to identify the executable status of automatic surgery and improve the safety of automatic surgery.
  • the application provides a method for determining the surgical grade, including:
  • the target automation level of the current operation is obtained according to the operation data of the current operation and the automation level classifier.
  • At least one functional procedure is performed in each surgery;
  • the training of the automatic level classifier of the operation according to the operation data of the historical operation includes: training the automatic level classifier of the corresponding functional operation according to the target data corresponding to each functional operation in the historical operation;
  • the obtaining the automation level of the current operation according to the operation data of the current operation and the automation level classifier includes: obtaining the automation level classifier according to the target data corresponding to the functional operation in the current operation and the corresponding automation level classifier. Describe the target level of automation for the functional operations performed by the current procedure.
  • the step of training the automatic level classifier of the corresponding functional operation according to the target data corresponding to each functional operation in the historical surgery includes:
  • the automation level classifier corresponding to the corresponding functional operation is trained.
  • the step of dividing the automation level corresponding to each of the functional operations in the historical operation includes: dividing the automation level corresponding to each of the functional operations in the historical operation according to postoperative evaluation .
  • the step of dividing the automation level corresponding to each of the functional operations in the historical operations according to the postoperative evaluation includes: sorting the postoperative evaluations of the historical operations according to predetermined rules, and according to The sorting result divides the automation level corresponding to each functional operation.
  • the step of obtaining the target automation level of the functional operation in the current operation according to the target data corresponding to the functional operation in the current operation and the corresponding automation level classifier includes:
  • the target data of the functional operation includes first target data and second target data; the inputting the data feature of the target data corresponding to the functional operation in the current operation into a corresponding The automation level classifier, to obtain the step of the target automation level of the functional operation comprises:
  • the first target data is data related to automatic surgery.
  • the comparing the first automation level with the second automation level to obtain the target automation level of the functional operation of the current operation includes:
  • the target automation level of the functional operation of the current operation is obtained is the first automation level; if the types of surgical actions allowed to be automatically performed in the first automation level are more than the types of surgical actions allowed to be automatically performed in the second automation level, an error message is generated.
  • the target data includes at least one of operation difficulty of the corresponding operation, intraoperative blood loss, complexity of the operation environment, and criticality of the patient's condition.
  • the method for determining the operation level further includes: displaying the target automation level of the current operation on a display unit.
  • the method before the step of training the automatic level classifier of surgery according to the surgical data of historical surgery, the method further includes: acquiring the surgical data of historical surgery, and storing the surgical data of historical surgery in a structured manner.
  • the method further includes:
  • the target automation level is sent to a surgical operation device, so that the surgical operation device performs a corresponding surgical operation according to the target automation level.
  • the present application also provides a surgical grade determination device, including:
  • the determination module is used to obtain the target automation level of the current operation according to the operation data of the current operation and the automation level classifier.
  • the training module includes:
  • An acquisition unit configured to acquire target data corresponding to each functional operation from the surgical data of the historical surgery, and acquire data characteristics of the target data
  • a division unit configured to divide the automation level corresponding to each of the functional operations in the historical operation, to obtain an automation level division result
  • the training unit is configured to train the corresponding automation level classifier according to the data characteristics of the target data corresponding to each of the functional operations in the historical operation and the automation level division result.
  • the present application also provides a surgical robot system, including a surgical operation device and the aforementioned surgical level determination device, the surgical operation device communicates with the surgical level determination device, and is used to Perform corresponding surgical operations according to the target automation level of the current surgery.
  • the present application also provides an electronic device, including a memory and a processor, the memory stores a computer program that can run on the processor, and when the processor executes the computer program, it realizes The method for determining the surgical grade as described in any one of the preceding items.
  • the present application also provides a computer-readable storage medium, on which a program is stored, and when the program is executed, the operation level determination method as described in any one of the preceding items is executed .
  • the automatic determination method, device, system, equipment and medium of the present application have the following advantages:
  • the aforementioned method for determining the operation level includes the following steps: training a classifier for the operation automation level according to the target data of the historical operation; and obtaining the automation level of the current operation according to the target data of the current operation and the classifier.
  • the method for determining the operation level is applied to a surgical robot system to identify the executable status of automatic surgery in the current operation, so that the surgical operation device can perform automatic surgery under the restriction of the corresponding automation level, improving the safety of automatic surgery .
  • Figure 1 is a schematic diagram of an application scenario of a surgical robot system
  • Fig. 2 is a structural schematic diagram of a doctor-end control device of the surgical robot system
  • Fig. 3 is a structural schematic diagram of the operation device at the operation end of the surgical robot system and the buzzer arranged thereon;
  • Fig. 4 is a structural schematic diagram of an image display device of the surgical robot system and a voice prompt system arranged thereon;
  • Fig. 5 is a flow chart of the method for determining the surgical level provided by the present application according to Embodiment 1;
  • FIG. 6 is a schematic diagram of surgical data of historical surgeries obtained in the method for determining the surgical grade provided in Embodiment 1 of the present application;
  • FIG. 7 is a flow chart of the classifier for training the surgical automation level in the method for determining the surgical level provided by the present application according to Embodiment 1;
  • Fig. 8 is a flow chart of acquiring the target data of the current surgery and obtaining the data characteristics of the target data in the method for determining the surgical level provided by the first embodiment of the present application;
  • Fig. 9 is a schematic diagram of the operation data of the current operation obtained in the method for determining the operation level provided in the first embodiment of the present application;
  • Fig. 10 is a schematic diagram of processing the operation data of the current operation in the operation level determination method provided by the present application according to the first embodiment
  • FIG. 11 is a flow chart of judging the automation level of the current surgery based on the surgical data of the current surgery in the method for determining the surgical level provided by the present application according to Embodiment 1;
  • Fig. 12 is a schematic diagram of displaying the automation level of the current surgery through the display of the image display device or the doctor-side control device in the method for determining the surgery level provided in Embodiment 1 of the present application;
  • Fig. 13 is a schematic diagram of the structure of the control unit of the surgical robot system provided by the present application according to Embodiment 4, and the connection relationship between the control unit, the display unit, and the surgical execution device;
  • FIG. 14 is a flow chart of the automatic surgery performed by the surgical robot system according to the fourth embodiment of the present application.
  • Fig. 15 is a schematic diagram of the current automated surgical operation displayed by the image display device of the surgical robot system or the display of the doctor's control device according to the fourth embodiment of the present application;
  • 10-doctor control device 20-patient control device, 30-surgical operation device, 31a-tool arm, 31b-image arm, 40-image display device, 41-voice prompt system, 50-control unit, 51-training Module, 51a-obtaining unit, 51b-dividing unit, 51c-training unit, 52-storage module, 53-determining module, 60-surgical instrument.
  • each embodiment of the content described below has one or more technical features, but this does not mean that the applicant must implement all the technical features in any embodiment at the same time, or can only implement different embodiments separately. Some or all of the technical features. In other words, on the premise that the implementation is possible, those skilled in the art can selectively implement some or all of the technical features in any embodiment according to the disclosure of the application and depending on the design specifications or implementation requirements, or Selectively implement a combination of some or all of the technical features in multiple embodiments, thereby increasing the flexibility of implementing the present application.
  • the singular forms “a”, “an” and “the” include plural objects, and the plural form “a plurality” includes two or more objects, unless the content clearly states otherwise.
  • the term “or” is generally used in the sense including “and/or”, unless the content clearly indicates otherwise, and the terms “install”, “connect” and “connect” should be To understand it in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral connection. It can be a mechanical connection or an electrical connection. It can be directly connected or indirectly connected through an intermediary, and it can be the internal communication of two elements or the interaction relationship between two elements. Those of ordinary skill in the art can understand the specific meanings of the above terms in this application according to specific situations.
  • Fig. 1 shows a schematic diagram of an application scenario of a surgical robot system
  • Figs. 2 to 4 show structural schematic diagrams of different devices in the surgical robot system.
  • the surgical robot system includes a control terminal and an execution terminal
  • the control terminal includes a doctor's console and a doctor's control device 10 arranged on the doctor's console
  • the doctor's control device 10 has an immersive display.
  • the executive end includes a patient-end control device 20 (marked in FIG. 13 ), a surgical operation device 30 , an image display device 40 and other equipment.
  • the surgical operation device 30 includes a plurality of mechanical arms, at least one of which is used as a tool arm 31a, and at least one of which is used as an image arm 31b.
  • the tool arm 31a is used to mount a surgical instrument 60 to perform surgical operations in the patient's body.
  • the image arm 31b is used to mount an image acquisition device, such as an endoscope (not shown in the figure), and the endoscope is used to enter the patient's body and acquire image information in the body.
  • a buzzer (not shown in the figure) is also provided on the surgical operation device.
  • the image display device 40 is communicatively connected with the endoscope, and is used for receiving and displaying the images acquired by the endoscope, so that the medical staff can observe the situation in the patient's body.
  • the image display device 40 is also provided with a voice prompt system 41 .
  • the surgical robot system may be a master-slave mapping robot system, that is, the doctor-side control device 10 further includes a master operator, and there is a predetermined distance between the master operator and the surgical operation device 30 . Therefore, the two can establish a master-slave relationship, so that the tool arm 31a and the surgical instrument 60 can realize actions in various directions according to the movement of the master operator. Not only that, in some cases, the master-slave relationship between the master operator and the surgical operation device 30 can also be broken, and the surgical operation device 30 can be directly or indirectly controlled by other means to perform automatic surgical operations.
  • the surgical operation device 30 Before the surgical operation device 30 performs an automatic surgical operation, it is necessary to execute a surgical level determination method through a control unit 50 (marked in FIG. 13 ) to determine the automation level of the current operation, so that the surgical operation device 30 can Perform automated surgical operations within the limits of a defined level of automation, improving surgical safety.
  • the method for determining the surgical grade includes the following steps:
  • Step S10 Train an automatic grade classifier for surgery according to the surgical data of historical surgery. as well as,
  • Step S20 Obtain the target automation level of the current operation according to the operation data of the current operation and the automation level classifier.
  • the method for determining the surgical level further includes step S00, step S30, and step S40, wherein the step S00 is performed before the step S10, and includes: acquiring surgical data of historical surgeries, and The data is stored in a structured manner to establish a surgical information database of historical surgeries.
  • the step S30 is executed after the step S20, and includes: displaying the automation level of the current operation on a display unit.
  • the step S40 can be executed synchronously with the step S30, which includes: sending the target automation level of the current operation to the surgical operation device 30, so that the surgical operation device 30 can perform corresponding operations according to the target automation level. surgical operation.
  • the operation data of the historical operation may be relevant operation data of each operation in multiple operations in the past, including but not limited to: automatic operation information, patient information, operation information, operating room information.
  • the automatic operation information includes, but is not limited to, the intervention status of the automatic operation, the operation difficulty of the automatic operation, the use information of the automatic operation, and the like.
  • the patient information includes, but is not limited to, information such as patient signs, criticality of the patient's condition, postoperative recovery of the patient, and the like.
  • the operation information includes but not limited to operation duration, various image information during the operation, movement information of the tool arm 31a and/or image arm 31b, intraoperative blood loss, postoperative evaluation and other information.
  • the operating room information includes, but is not limited to, information such as equipment and layout of the operating room.
  • the embodiment of the present application has no special limitation on the acquisition method of the operation data of the historical operation, for example, the operation data can be stored automatically in real time by the control unit 50 of the robot system during the historical operation, or The data can be manually input into the control unit 50 by the medical personnel after the operation, or the operation data can be transmitted to the control unit 50 by other control mechanisms in a wired or wireless manner.
  • the step S10 actually refers to training the automatic level classifier of the corresponding functional operation according to the target data of each functional operation in the historical operation, for example, training the first functional operation according to the target data of the first functional operation in the historical operation.
  • An automatic grade classifier for functional operations, training the automatic grade classifier for the second functional operation according to the target data of the second functional operation in the historical operation, and training the automatic grade classifier for the third functional operation according to the target data of the third functional operation in the historical operation Automated classifiers for trifunctional operations, etc.
  • step S10 may include the following steps:
  • Step S11 extracting the target data corresponding to each functional operation from the surgical data of the historical surgery, and acquiring data features of the target data.
  • Step S12 Carry out automatic level classification for each of the functional operations of the historical operations.
  • Step S13 according to the data characteristics of the target data of each functional operation and the automation level of the corresponding functional operation, the corresponding automation level classifier is trained.
  • the target data in the step S11 refers to data that can affect the automation level of the functional operation.
  • the target data may be at least one of the operational difficulty of the corresponding operation, the amount of blood loss during the operation, the complexity of the operation environment, and the criticality of the patient's condition.
  • the target data can be determined through big data analysis of all historical operation data. The specific analysis method is familiar to those skilled in the art and will not be introduced this time. Certainly in some cases, described target data also can be determined by medical personnel according to actual situation and experience.
  • the data characteristics of the target data refer to the level of the corresponding target data, for example, the data characteristics of the target data have three levels: high, medium, and low, and the data characteristics of different levels are represented by Different numbers are marked.
  • the data feature of the target data is at a high level, it is marked with a number 2; when the data feature of the target data is at a medium level, it is marked with a number 1; when the feature of the target data is At low levels, it is marked with the number 0.
  • the data characteristic of the operation difficulty of the automated surgery is a high level, and marked as 2; when the operation difficulty of the automated surgery is medium
  • the data feature of the operational difficulty of the automated surgery is a medium level, which is marked as 1; when the automatic operation difficulty is low, the data feature of the operational difficulty of the automated surgery is a low level, which is marked as 0.
  • the intraoperative blood loss is marked as 2; when the intraoperative blood loss is moderate, the data feature of the intraoperative blood loss is marked as 1; when the intraoperative blood loss is small, the intraoperative The data characteristic of blood loss is marked as 0.
  • the data feature of the complexity of the surgical environment is marked as 2; when the complexity of the surgical environment is medium, the data feature of the complexity of the surgical environment is marked as 1; when the surgical environment is simple, the complexity of the surgical environment is The data features of are marked as 0.
  • the data feature of the critical degree of the patient's illness is marked as 2; when the patient's illness is moderately critical, the data feature of the critical degree of the patient's illness is marked as 1; when the patient's illness is not critical, The data feature of criticality of the patient's condition is marked as 0.
  • Those skilled in the art can determine the level division standard of the data features after performing big data analysis on all the target data of historical operations. The specific analysis method is familiar to those skilled in the art, and will not be introduced this time.
  • each of the functional operations of the historical surgery may be automatically graded according to the postoperative evaluation of the historical surgery.
  • the automation level can be divided into three levels, namely level 3, level 2, and level 1, and the lower the level, the fewer types of actions that the surgical operation device 30 is allowed to automatically perform, that is, level 3 Level 2 allows the surgical operation device 30 to perform the most actions automatically, level 2 allows the surgical operation device 30 to automatically perform the next action, and level 1 allows the surgical operation device 30 to automatically perform the least actions.
  • control unit 50 When automatically classifying the functional operations in historical operations, the control unit 50 first evaluates all the postoperative operations of historical operations according to predetermined rules, for example, the evaluation of a specified functional operation is in order from good to bad Sort, and then divide the automation level of the specified functional operation in the top 30% of historical operations into 3 levels, and rank the specified functional operation in 31% to 70% of historical operations The automation level of the specified functional operation in the historical operations ranked 71%-100% is divided into level 1.
  • the predetermined rule is to sort the historical surgeries according to the evaluation of the specified functional operations in order from bad to good, then the specified Classify the automation level of the functional operation as Level 1, divide the automation level of the specified functional operation in the historical surgery ranked as 31% to 70% as Level 2, and classify the automation level of the specified functional operation in the historical operation as ranked 71% to 100
  • the automation level of the specified functional operation in the historical operation is divided into level 3.
  • each functional operation has four target data, and the four target data are the operational difficulty of automatic surgery, intraoperative blood loss, the complexity of the surgical environment, and the criticality of the patient's illness,
  • the following table 1 can be obtained.
  • the first functional operation is numbered 1
  • the second functional operation is numbered 2
  • the third functional operation is numbered The number is 3.
  • the control unit 50 executes the step S13, that is, obtains the mapping relationship between the data feature of the target data of the functional operation and the corresponding automation level through the training of the automation level classifier.
  • this article only takes the first functional operation in each historical operation as an example.
  • Table 2 shows the data characteristics of the target data of the first functional operation in ten historical operations and the automation level of the first functional operation in each historical operation.
  • any suitable classification algorithm can be used to carry out the training of the automatic grade classifier, and optional algorithms include but not limited to decision tree algorithm, naive Bayesian algorithm, logistic regression algorithm, neural network algorithm etc.
  • the naive Bayesian algorithm is used to train the classifier.
  • the naive Bayesian algorithm calculate the first functional operation according to the following formula (I)
  • the probability of automation level of that is to calculate the probability P(yk
  • the automation level corresponding to the probability maximum value is used as the automation level of the functional operation, and the calculation formula is the following formula (III):
  • the first functional operation in all historical operations in Table 2 is calculated until the automatic level classifier training of the first functional operation is completed and stored . It can be understood that the control unit 50 can use the same method to obtain other automatic classifiers with functional operations.
  • the step S20 actually obtains the automation level of each functional operation of the current operation according to the target data of each functional operation of the current operation and the corresponding automation level classifier, for example, according to the first functional operation in the current operation.
  • the target data and the automation level classifier of the first functional operation obtain the automation level of the first functional operation of the current operation, the target data of the second functional operation in the current operation and the automation level classifier of the second functional operation Obtain the automation level of the second functional operation of the current operation, obtain the automation level of the third functional operation of the current operation according to the target data of the third functional operation in the current operation and the automation level classifier of the third functional operation, etc. .
  • the step S20 may specifically include the following steps:
  • Step S21 Obtain the operation data of the current operation.
  • Step S22 Extracting the target data of each of the functional operations from the surgical data of the current operation, and acquiring data features of the target data. as well as,
  • Step S23 input the data features of the target data of each functional operation of the current operation into a corresponding automation level classifier, and obtain the corresponding automation level of each functional operation.
  • the operation data of the current operation includes the pre-operation data and/or the intra-operation data of the current operation.
  • the preoperative data includes patient information, operation information, automatic operation information, and operation room information, where the patient information includes information such as the patient's signs, the criticality of the patient's condition, and the operation information includes, for example, the estimated duration of the operation, the estimated duration of the operation, etc.
  • the amount of blood loss and other information, and the automated surgery information includes information such as the estimated difficulty of the automated surgery operation.
  • the intraoperative data are real-time surgical data, including image information collected by the endoscope, movement information of the robotic arm of the surgical operating device 30 , automatic surgery information, intraoperative blood loss, and other information. Surgical information for the current surgery may be obtained in any suitable manner. Those skilled in the art should understand that using the preoperative surgical data to obtain the automation level of the current operation is actually to predict the automation level of a certain functional operation before the operation.
  • the target data in the step S22 is the same as the target data in the step S11, which is also the operation difficulty of the automatic surgery, the amount of blood loss during the operation, the complexity of the operation environment, and the criticality of the patient's illness at least one of the .
  • step S22 actually includes:
  • Step S22a Cleaning and denoising the operation data of the current operation to remove non-target data and retain the target data of the functional operation of the current operation. as well as,
  • Step S22b Obtain and store the data characteristics of the target data of the current operation through big data analysis.
  • the target data of the current operation is divided into two parts, namely first target data and second target data, wherein the first target data refers to data related to automatic surgery.
  • the data related to the automatic surgery can be determined by the medical staff according to the actual situation.
  • the automatic surgery Relevant data include the operational difficulty of automated surgery.
  • step S23 may include the following steps:
  • Step S23a Input the data features of all target data (ie, the first target data and the second target data) of the functional operation of the current operation into the corresponding automatic classifier, and obtain the functional operation of the current operation First level of automation for sexual manipulation.
  • target data ie, the first target data and the second target data
  • Step S23b Input the data features of the second target data of the functional operation of the current operation into a corresponding automation level classifier, and obtain the second automation level of the functional operation of the current operation.
  • Step S23c comparing the first automation level with the second automation level, and obtaining the automation level of the functional operation of the current operation.
  • step S23c if the types of surgical actions that are allowed to be automatically performed by the surgical operation device 30 in the first automation level are less than or equal to the types of operations that are allowed to be automatically performed by the surgical operation device 30 in the second automation level type of action, then determine the automation level of the functional operation of the current surgery as the first automation level; In the second automation level, there are many types of surgical actions that the surgical operation device 30 is allowed to perform automatically, and an error message is generated to remind the medical staff that the functional operation cannot be performed at the first automation level. The purpose of doing this is to consider the safety of the functional operation of the current operation when it is automatically performed from multiple dimensions.
  • the data features of the four target data of the first functional operation in the current operation are all 1, that is, the data feature of the operation difficulty of automatic surgery is 1, the data feature of intraoperative blood loss is 1, and the surgical environment
  • the complexity of , and the criticality of the patient's condition is also 1.
  • the automation level classifier of the first functional operation is trained according to the historical operation data shown in Table 2, and the automation level of the first functional operation in the current operation is calculated accordingly. The process is as follows:
  • the probabilities that the first automation level of the first functional operation is level 1, level 2, and level 3 are 0.444, 0.579, and 0, respectively, so that the first automation level of the first functional operation can be determined.
  • the first level of automation is level 2.
  • the probabilities that the second automation level of the first functional operation is level 1, level 2, and level 3 are 0.296, 0.926, and 0, respectively, so that the automation level of the first functional operation can be determined.
  • the second level of automation is level 2. Therefore, in the current operation, the target automation level of the first functional operation is level 2.
  • the display unit includes the image display device 40 and/or the immersive display of the doctor terminal control device 10, that is, as shown in FIG. 12, the image display device 40 displays the current The automation level of the functional operation of the operation, or for example, the immersive display of the doctor-side control device 10 displays the automation level of the functional operation of the current operation (not shown in the figure).
  • other information is also displayed on the display unit, such as the automatic operation level function switch, before or during the operation, the automatic operation level function switch is turned on, so that the control unit 50 performs operation according to the operation data of the current operation. Judgment of the automation level of the corresponding functional operation (judgment based on preoperative surgical data, or judgment based on intraoperative surgical data).
  • the surgical level determination method is executed first, and the target automation level of the current surgery can be determined in advance, so that the surgical operation device can perform corresponding surgical operations under the determined target automation level , improve the safety and reliability of automatic surgery, and avoid unnecessary harm to patients.
  • the automatic surgical level determining device includes a training module 51 and a determining module 53, and preferably also includes a storage module 52, the training module 51 is used for training The automation level classifier of the operation, the storage module 52 is used to store the automation level classifier, and the determination module 53 is used to obtain the target automation of the current operation according to the operation data of the current operation and the automation level classifier. grade.
  • the device for determining the surgical level may be integrated into a control mechanism, such as the control unit 50 of the surgical robot system.
  • the training module 51 includes an acquisition unit 51a, a division unit 51b and a training unit 51c.
  • the acquiring unit 51a is configured to acquire the target data corresponding to each functional operation from the surgical data of the historical surgery, and acquire the data characteristics of the target data.
  • the division unit 51b is configured to divide the automation level corresponding to each of the functional operations in the historical operations.
  • the division unit 51b is configured to divide the automation level corresponding to each of the functional operations in the historical operation according to the postoperative evaluation. For example, the postoperative evaluations of the historical operations may be firstly sorted according to predetermined rules, and then the division unit 51b divides the automation level corresponding to each of the functional operations according to the sorting structure.
  • the training unit 51c is configured to train the corresponding automation level classifier according to the data characteristics of the target data corresponding to each of the functional operations in the historical operation and the automation level classification results. After the acquisition unit 51a acquires the data features of the target data, and the training unit 51c trains the corresponding automatic level classifier, the determination module 53 according to the current operating procedure input to the corresponding automatic level classifier The target data feature corresponding to the functional operation of the current operation is obtained to obtain the target automation level of the functional operation of the current operation.
  • the target data includes the first target data and the second target data
  • the target data may specifically include the operation difficulty of the corresponding operation, the amount of blood loss during the operation, the complexity of the operation environment, and the degree of danger to you from the patient's illness etc.
  • the first target data is data related to automatic surgery, for example, corresponding to the operation difficulty of the surgery.
  • the determination module 53 When determining the target automation level of the functional operation of the current operation, the determination module 53 is first used for all target data of the functional operation of the current operation according to the input corresponding automation level classifier Data characteristics, obtaining the first automation level of the functional operation of the current operation. Then according to the second target data input into the corresponding automation level classifier, the second automation level of the functional operation of the current operation is obtained. Finally, the first automation level is compared with the second automation level to obtain a target automation level of the functional operation of the current operation.
  • the determination module 53 may also be communicatively connected with the display unit (such as the image display device 40 and/or the doctor-side control device 10) and the surgical operation device 30, so as to determine the target automation level of the current operation Afterwards, the target automation level is sent to the display unit for display, and sent to the surgical operation device 30.
  • the determination module 53 may first send the target automation level to the surgical operation device 30 , so that the surgical operation device 30 performs a corresponding surgical operation at a determined target automation level.
  • the acquisition unit 51a is also used to acquire historical operation data.
  • the storage module 52 is communicatively connected with the acquisition unit 51a for structured storage of the operation data of the historical operations.
  • the surgical robot system not only includes the control terminal and the execution terminal as shown in FIG. 1 , but also includes a surgical level determination device as shown in FIG. 13 .
  • the surgical level determination device can be integrated into the control unit 50 of the surgical robot system.
  • control unit 50 can be integrally disposed at the doctor-end control device 10, or integrally disposed at the patient-end control device 20, or partly set at the doctor-side control device 10, partly set at the patient-side control device 20, or independent of the patient-side control device 20 and the doctor-side control device 10, as long as it can Execute the corresponding function. It should be noted that when the control unit 50 is independent from the patient-side control device 20 and the doctor-side control device 10, the control unit 50 is also connected with the doctor-side control device 10 and the patient-side control device 10. The control device 20 is connected in communication.
  • the acquisition unit 51a is also used to acquire the postoperative operation data of the current operation to update the history Surgical operation data information database (as shown in Figure 9 and Figure 10 ), the postoperative operation data includes postoperative evaluation, postoperative recovery of the patient, and the like.
  • the voice prompt system can broadcast the information of starting or ending the automatic surgery, and the control unit 50 or the doctor-end control device or the patient
  • the terminal control device can also judge whether the current automatic surgery operation is safe and the corresponding degree of danger, and give an alarm through the voice prompt system and/or the buzzer.
  • Fig. 14 shows a flow chart of the automatic surgery performed by the surgical robot system. After it is determined to perform the automatic surgery, as shown in Figure 14, the process of the automatic surgery includes:
  • Step S1 The voice prompt system makes a voice broadcast to remind the medical staff that the surgical operation device 30 will perform an automatic surgical operation next.
  • the content of the voice announcement may be, for example, "automatic surgery is about to be performed at a certain level” and the like.
  • Step S2 The surgical operation device 30 performs automatic surgical operation under the limitation of the determined automation level.
  • Step S3 judge in real time whether the current operation is safe, if yes, the buzzer will not send out an alarm, if not, go to step S4.
  • Step S4 Determine the degree of danger of the current surgical operation. If the degree of danger is high, the buzzer will send out a first alarm at the first frequency to prompt the intervention operation. If the degree of danger is low, the buzzer will send out the first alarm at the first frequency. Second frequency to sound the second alarm.
  • Step S5 The medical personnel manually intervene and judge whether to use the emergency stop function to exit the automatic operation, if not, go back to step S2, and if yes, go to step S6.
  • the activation switch of the emergency stop function can be displayed on the immersive display of the doctor terminal control device 10 .
  • Step S6 Exit the automatic operation, and perform voice broadcast.
  • step S3 and the step S4 can be performed according to the safety protection measures of the surgical robot system in the prior art, for example, in step S3, the current surgical action is judged by judging whether the movement speed of the surgical instrument 60 exceeds the limit. Whether it is safe, and in the step S4, the degree of danger of the current surgical action is judged according to the degree of overrun of the movement speed of the surgical instrument 60 .
  • the display unit such as the image display device 40 can also display the image information in the patient's body collected by the endoscope, so that the medical staff can intuitively Observe the current automated surgical operation.
  • This embodiment provides a computer-readable storage medium, on which a program is stored.
  • the program is executed, the method for determining the operation level provided in Embodiment 1 is executed.
  • computer readable storage may then include, but is not limited to, portable disks, hard disks, random access memory, read only memory, erasable programmable read only memory, optical memory, magnetic memory, or any suitable combination of the foregoing.
  • This embodiment provides an electronic device, the electronic device includes a memory and a processor, the memory stores a computer program that can run on the processor, and the processor is configured to implement the computer program when executing the computer program
  • the components of the electronic device include but are not limited to at least one memory and at least one processor, and may also include a display unit, and a bus connecting these components.
  • the bus includes a data bus, an address bus and a control bus.
  • the memory may include volatile memory, such as random access memory (RAM) and/or cache memory, and may further include read only memory (ROM).
  • RAM random access memory
  • ROM read only memory
  • the memory may also include programs/utilities having a set (at least one) of program modules including, but not limited to, an operating system, one or more application programs, other program modules, and program data, in which case Each or some combination may include implementations of network environments.
  • the processor executes various functional applications and data processing by running the computer program stored in the memory, such as the method for determining a surgical grade provided in Embodiment 1 of the present application.
  • the electronic device may also communicate with one or more external devices such as a keyboard, pointing device, and the like. Such communication may occur through input/output (I/O) interfaces.
  • the electronic device can also communicate with one or more networks such as a local area network (LAN), a wide area network (WAN) and/or a public network through a network adapter.
  • networks such as a local area network (LAN), a wide area network (WAN) and/or a public network through a network adapter.
  • LAN local area network
  • WAN wide area network
  • other hardware and/or software modules may be used in conjunction with the electronic device, including but not limited to microcode, device drivers, redundant processors, external disk drive arrays, disk array systems, tape drives, and data backup storage systems, etc. .

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Abstract

本申请提供了一种手术等级确定方法、装置、系统、设备及介质，所述手术等级确定方法包括如下步骤：根据历史手术的手术数据构建手术的自动化等级分类器；以及，根据当前手术的手术数据以及所述自动化等级分类器得到当前手术的自动化等级。该手术等级确定方法可应用于手术机器人系统，并在执行自动手术之前判断自动手术的自动化等级，进而在确定的自动化等级的限制下执行自动手术操作，提高自动手术的安全性。

Description

手术等级确定方法、装置、系统、设备及介质 技术领域
本申请涉及医疗器械技术领域，具体涉及一种手术等级确定方法、装置、系统、设备及介质。
背景技术
手术机器人的设计理念是采用微创伤的方式精准地实施复杂的外科手术。在传统的手术操作面临种种局限的情况下发展出现了手术机器人，手术机器人突破了人眼的局限，其能够利用立体成像技术将人体内部的器官更加清晰地呈现给施术者。并且对于一些人的手部无法伸入的狭小区域，手术机器人仍可控制手术器械完成挪动、摆动、夹持及360°转动，并可避免抖动，提高手术精确度，进一步达到创口小、出血少、术后恢复快、极大地缩短手术对象术后住院时间的优势。因此，手术机器人深受广大医患的青睐，广泛应用于各自临床手术中。
随着现代化医疗技术的发展，部分特定的手术操作可以由机器人自主执行，这些自主操作适用于某些特定的手术环境中，但现有技术中在应用机器人自动地手术操作时，没有对机器人的自主操作进行安全等级的限制，这容易带来安全隐患。
发明内容
本申请的目的在于提供一种手术等级确定方法、装置、系统、设备及介质，以辨别自动手术的可执行情况，并提高自动手术的安全性。
为实现上述目的，本申请提供了一种手术等级确定方法，包括：
根据历史手术的手术数据训练手术的自动化等级分类器；以及，
根据当前手术的手术数据以及所述自动化等级分类器得到当前手术的目标自动化等级。
可选地，每一台手术中执行至少一种功能性操作；
所述根据历史手术的手术数据训练手术的自动化等级分类器包括：根据所述历史手术中的各功能性操作对应的目标数据训练相应功能性操作的自动化等级分类器；
所述根据当前手术的手术数据以及所述自动化等级分类器得到当前手术的自动化等级包括：根据所述当前手术中的功能性操作所对应的目标数据以及相应的所述自动化等级分类器，得到所述当前手术执行的功能性操作的目标自动化等级。
可选地，所述根据所述历史手术中的各功能性操作对应的目标数据训练相应功能性操作的自动化等级分类器的步骤包括：
从所述历史手术的手术数据中提取各所述功能性操作对应的所述目标数据，并获取所述目标数据的数据特征；
对所述历史手术中的各所述功能性操作对应的自动化等级进行划分，得到自动化等级划分结果；以及，
根据所述历史手术中的各所述功能性操作对应的所述目标数据的数据特征以及所述自动化等级划分结果，对相应功能性操作对应的所述自动化等级分类器进行训练。
可选地，对所述历史手术中的各所述功能性操作对应的自动化等级进行划分的步骤包括：根据术后评价对所述历史手术中的各所述功能性操作对应的自动化等级进行划分。
可选地，所述根据术后评价对所述历史手术中的各所述功能性操作对应的自动化等级进行划分的步骤包括：将所述历史手术的术后评价按照预定规则进行排序，并按照排序结果对各所述功能性操作对应的自动化等级进行划分。
可选地，所述根据当前手术中的功能性操作所对应的目标数据及相应的所述自动化等级分类器得到所述当前手术中的功能性操作的目标自动化等级的步骤包括：
从所述当前手术的手术数据中提取所述功能性操作对应的所述目标数据，并获取所述目标数据的数据特征；以及，
将所述当前手术中的所述功能性操作对应的所述目标数据的数据特征输入相应的所述自动化等级分类器，以得到所述当前手术的所述功能性操作的目标自动化等级。
可选地，所述功能性操作的所述目标数据包括第一目标数据和第二目标数据；所述将当前手术中的所述功能性操作对应的所述目标数据的所述数据特征输入相应的所述自动化等级分类器，以得到所述功能性操作的目标自动化等级的步骤包括：
将所述当前手术的所述功能性操作的所有目标数据的数据特征输入相应的所述自动化等级分类器，并得到当前手术的所述功能性操作的第一自动化等级；
将所述第二目标数据的数据特征输入相应的所述自动化等级分类器，并得到所述当前手术的所述功能性操作的第二自动化等级；
对所述第一自动化等级和所述第二自动化等级进行比较，得到所述当前手术的所述功能性操作的目标自动化等级；
其中，所述第一目标数据为自动手术相关数据。
可选地，所述对所述第一自动化等级和所述第二自动化等级进行比较，得到当前手术的所述功能性操作的目标自动化等级，包括：
若所述第一自动化等级中允许自动执行的手术动作的类型少于或等于所述第二自动化等级中允许自动执行的手术动作的类型，则得到当前手术的所述功能性操作的目标自动化等级为所述第一自动化等级；若所述第一自动化等级中允许自动执行的手术动作的类型多于所述第二自动化等级中允许自动执行的手术动作的类型，则产生错误信息。
可选地，所述目标数据包括对应手术的操作难度、术中出血量、手术环境的复杂程度、以及患者病症的危急程度中的至少一种。
可选地，所述手术等级确定方法还包括：使所述当前手术的目标自动化等级在一显示单元上进行显示。
可选地，在所述根据历史手术的手术数据训练手术的自动化等级分类器的步骤之前，还包括：获取所述历史手术的手术数据，并对所述历史手术的手术数据进行结构化存储。
可选地，在所述根据当前手术的手术数据以及所述自动化等级分类器得到当前手术的目标自动化等级之后，还包括：
将所述目标自动化等级发送至手术操作装置，以使所述手术操作装置根据所述目标自动化等级执行相应的手术操作。
为实现上述目的，本申请还提供了一种手术等级确定装置，包括：
训练模块，用于根据历史手术的手术数据训练手术的自动化等级分类器；
确定模块，用于根据当前手术的手术数据以及所述自动化等级分类器得到当前手术的目标自动化等级。
可选地，所述训练模块包括：
获取单元，用于从所述历史手术的手术数据中获取各功能性操作对应的目标数据，并获取所述目标数据的数据特征；
划分单元，用于对所述历史手术中的各所述功能性操作对应的自动化等级进行划分，得到自动化等级划分结果；以及，
训练单元，用于根据所述历史手术中的各所述功能性操作对应的所述目标数据的数据特征以及所述自动化等级划分结果，对相应的所述自动化等级分类器进行训练。
为实现上述目的，本申请还提供了一种手术机器人系统，包括手术操作装置以及如前所述的手术等级确定装置，所述手术操作装置与所述手术等级确定装置通信连接，并用于根据所述当前手术的所述目标自动化等级执行相应的手术操作。
为实现上述目的，本申请还提供了一种电子设备，包括存储器和处理器，所述存储器上存储有可在所述处理器上运行的计算机程序，所述处理器执行所述计算机程序时实现如前任一项所述的手术等级确定方法。
为实现上述目的，本申请还提供了一种计算机可读存储介质，所述计算机可读存储介质上存储有程序，当所述程序被执行时，执行如前任一项所述的手术等级确定方法。
与现有技术相比，本申请的手术等级自动化确定方法、装置、系统、设备及介质具有如下优点：
前述的手术等级确定方法包括如下步骤：根据历史手术的目标数据训练手术自动化等级的分类器；以及，根据当前手术的目标数据和所述分类器得到当前手术的自动化等级。所述手术等级确定方法应用于手术机器人系统，以用于辨别当前手术中自动手术的可执行情况，进而使得手术操作装置在相应的自动化等级的限制下执行自动手术操作，提高自动手术的安全性。
附图说明
附图用于更好地理解本申请，不构成对本申请的不当限定。其中：
图1是手术机器人系统的应用场景示意图；
图2是手术机器人系统的医生端控制装置的结构示意图；
图3是手术机器人系统的手术端操作装置及设置于其上的蜂鸣器的结构示意图；
图4是手术机器人系统的图像显示装置及设置于其上的语音提示系统的结构示意图；
图5是本申请根据实施例一所提供的手术等级确定方法的流程图；
图6是本申请根据实施例一所提供的手术等级确定方法中获取的历史手术的手术数据的示意图；
图7是本申请根据实施例一所提供的手术等级确定方法中训练手术自动化等级的分类器的流程图；
图8是本申请根据实施例一所提供的手术等级确定方法中获取当前手术的目标数据并得到目标数据的数据特征的流程图；
图9是本申请根据实施例一所提供的手术等级确定方法中获取的当前手术的手术数据的示意图；
图10是本申请根据实施例一所提供的手术等级确定方法中对当前手术的手术数据进行处理的示意图；
图11的本申请根据实施例一所提供的手术等级确定方法中根据当前手术的手术数据判断当前手术的自动化等级的流程图；
图12是本申请根据实施例一所提供的手术等级确定方法中，通过图像显示装置或医生端控制装置的显示器对当前手术的自动化等级进行显示的示意图；
图13是本申请根据实施例四所提供的手术机器人系统的控制单元的结构，及控制单元与显示单元、手术执行装置的连接关系示意图；
图14是本申请根据实施例四所提供的手术机器人系统执行自动手术的流程图。
图15是本申请根据实施例四所提供的手术机器人系统的图像显示装置或医生端控制装置的显示器对当前的自动化手术操作进行显示的示意图；
[附图标记说明如下]：
10-医生端控制装置，20-患者端控制装置，30-手术操作装置，31a-工具臂，31b-图像臂，40-图像显示装置，41-语音提示系统，50-控制单元，51-训练模块，51a-获取单元，51b-划分单元，51c-训练单元，52-存储模块，53-确定模块，60-手术器械。
具体实施方式
以下通过特定的具体实例说明本申请的实施方式，本领域技术人员可由本说明书所揭露的内容轻易地了解本申请的其他优点与功效。本申请还可以通过另外不同的具体实施方式加以实施或应用，本说明书中的各项细节也可以基于不同观点与应用，在没有背离本申请的精神下进行各种修饰或改变。需要说明的是，本实施例中所提供的图示仅以示意方式说明本申请的基本构想，遂图式中仅显示与本申请中有关的组件而非按照实际实施时的组件数目、形状及尺寸绘制，其实际实施时各组件的型态、数量及比例可为一种随意的改变，且其组件布局型态也可能更为复杂。
另外，以下说明内容的各个实施例分别具有一或多个技术特征，然此并不意味着使用本申请者必需同时实施任一实施例中的所有技术特征，或仅能分开实施不同实施例中的一部或全部技术特征。换句话说，在实施为可能的前提下，本领域技术人员可依据本申请的公开内容，并视设计规范或实作需求，选择性地实施任一实施例中部分或全部的技术特征，或者选择性地实施多个实施例中部分或全部的技术特征的组合，借此增加本申请实施时的弹性。
如在本说明书中所使用的，单数形式“一”、“一个”以及“该”包括复数对象，复数形式“多个”包括两个以上的对象，除非内容另外明确指出外。如在本说明书中所使用的，术语“或”通常是以包括“和/或”的含义而进行使用的，除非内容另外明确指出外，以及术语“安装”、“相连”、“连接”应做广义理解，例如，可以是固定连接，也可以是可拆卸连接，或一体地连接。可以是机械连接，也可以是电连接。可以是直接相连，也可以通过中间媒介间接相连，可以是两个元件内部的连通或两个元件的相互作用关系。对于本领域的普通技术人员而言，可以根据具体情况理解上述术语在本申请中的具体含义。
为使本申请的目的、优点和特征更加清楚，以下结合附图对本申请作进一步详细说明。需说明的是，附图均采用非常简化的形式且均使用非精准的比例，仅用以方便、明晰地辅助说明本申请实施例的目的。附图中相同或相似的附图标记代表相同或相似的部件。
<实施例一>
图1示出了手术机器人系统的应用场景示意图，图2至图4示出了所述手术机器人系统中的不同设备的结构示意图。如图1至图4所示，所述手术机器人系统包括控制端和执行端，所控制端包括医生控制台和设置于所述医生控制台上的医生端控制装置10，所述医生端控制装置10具有一沉浸式显示器。所述执行端包括患者端控制装置20(如图13所标注)、手术操作装置30、图像显示装置40等设备。所 述手术操作装置30包括多条机械臂，其中，至少一条所述机械臂用作工具臂31a，至少一条所述机械臂用作图像臂31b。所述工具臂31a用于挂载手术器械60，以在患者体内执行手术操作。所述图像臂31b用于挂载图像获取装置，所述图像获取装置例如是内窥镜(图中未示出)，所述内窥镜用于进入患者体内，并获取体内的图像信息。所述手术操作装置上还设有蜂鸣器(图中未示出)。所述图像显示装置40与所述内窥镜通信连接，用于接收并显示所述内窥镜获取的图像，以便于医护人员观察患者体内的情况。所述图像显示装置40上还设有语音提示系统41。可选地，所述手术机器人系统可以为主从映射机器人系统，也即，所述医生端控制装置10还包括主操作手，且所述主操作手与所述手术操作装置30之间具有预定的映射关系，从而两者可以建立主从关系，以使得所述工具臂31a及所述手术器械60依据所述主操作手的运动来实现各个方向的动作。不仅如此，在一些情况下，所述主操作手与所述手术操作装置30之间也可以断开主从关系，并通过其他方式直接或间接地控制所述手术操作装置30进行自动手术操作。
在所述手术操作装置30执行自动手术操作之前，需通过一控制单元50(如图13所标注)执行一手术等级确定方法，以确定当前手术的自动化等级，进而可使所述手术操作装置30在确定的自动化等级的限制下执行自动手术操作，提高手术的安全性。
如图5所示，所述手术等级确定方法包括如下步骤：
步骤S10：根据历史手术的手术数据训练手术的自动化等级分类器。以及，
步骤S20：根据当前手术的手术数据和所述自动化等级分类器得到当前手术的目标自动化等级。
进一步地，所述手术等级确定方法还包括步骤S00、步骤S30和步骤S40，其中，所述步骤S00在所述步骤S10之前执行，并包括：获取历史手术的手术数据，并对历史手术的手术数据进行结构化存储，以建立历史手术的手术信息数据库。所述步骤S30在所述步骤S20之后执行，并包括：使当前手术的自动化等级在一显示单元上显示。所述步骤S40可与所述步骤S30同步执行，其包括：将当前手术的所述目标自动化等级发送至所述手术操作装置30，以使所述手术操作装置30根据所述目标自动化等级执行相应的手术操作。
接下来，本文对所述手术等级确定方法做更为详细的说明。
所述步骤S00中，如图6所示，所述历史手术的手术数据可以是往期的多台手术中每一台手术的相关手术数据，包括但不限于：自动手术信息、患者信息、手术信息、手术室信息。其中所述自动手术信息包括但不限于自动手术的介入情况、自动手术的操作难度、自动手术的使用信息等。所述患者信息包括但不限于患者体征、患者病症的危急程度、患者的术后恢复情况等信息。所述手术信息包括但不限于手术时长、手术过程中的各种图像信息、工具臂31a和/或图像臂31b的运动信息、术中出血量、术后评价等信息。所述手术室信息包括但不限于手术室的设备及布局等信息。
本申请实施例对历史手术的手术数据的获取方式没有特别限制，例如手术数据可以在历史手术过程中由所述机器人系统的所述控制单元50对手术数据实时自动地进行存储，或者所述手术数据可以由医护人员在手术结束之后手动地输入所述控制单元50，再或者，所述手术数据由其他的控制机构通过有线或无线的方式传输至所述控制单元50。
本领域技术人员应知晓，每一台手术中至少执行一个功能性操作，例如执行第一功能性操作、第二功能性操作、第三功能性操作等，所述功能性操作即是具体的手术操作，例如剪切、缝合等操作。因此，所述步骤S10实际上是指根据历史手术中的各个功能性操作的目标数据训练相应的功能性操作的自动化等级分类器，例如根据历史手术中的第一功能性操作的目标数据训练第一功能性操作的自动化等级分类器，根据历史手术中的第二功能性操作的目标数据训练第二功能性操作的自动化等级分类器，根据历史手术中的第三功能性操作的目标数据训练第三功能性操作的自动化等级分类器等。
如图7所示，所述步骤S10可包括如下步骤：
步骤S11：从所述历史手术的手术数据中提取各功能性操作对应的所述目标数据，并获取所述目标数据的数据特征。
步骤S12：对所述历史手术的各所述功能性操作进行自动化等级划分。
步骤S13：根据各所述功能性操作的所述目标数据的数据特征以及相应的功能性操作的自动化等级进行相应的自动化等级分类器的训练。
这里，所述步骤S11中的所述目标数据是指能够对所述功能性操作的自动化等级产生影响的数据。可选地，所述目标数据可以是相应手术的操作难度、术中出血量、手术环境的复杂程度、患者病症的危急程度中的至少一者。所述目标数据可以通过对所有的历史手术的手术数据进行大数据分析后确定，具体分析方法为本领域技术人员可以习知的内容，此次不做介绍。当然在某些情况下，所述目标数据也可 以由医护人员根据实际情况及经验确定。
在一种可选的实施方式中，所述目标数据的数据特征是指相应目标数据的级别，例如所述目标数据的数据特征具有高、中、低三种级别，且不同级别的数据特征以不同的数字进行标记。可选地，当所述目标数据的数据特征为高级别时，以数字2来标记，当所述目标数据的数据特征为中级别时，以数字1来标记，当所述目标数据的特征为低级别时，以数字0来标记。举例来说，在本申请实施例中，当所述自动化手术的操作难度高时，所述自动化手术的操作难度的数据特征为高级别，并标记为2；当所述自动化手术的操作难度中等时，所述自动化手术的操作难度的数据特征为中级别，并标记为1；当所述自动化操作难度小时，所述自动化手术的操作难度的数据特征为低级别，并标记为0。类似地，当术中出血量多时，术中出血量的数据特征标注为2，当术中出血量中等时，术中出血量的数据特征标注为1，当术中出血量少时，术中出血量的数据特征标注为0。当手术环境复杂时，手术环境的复杂程度的数据特征标注为2，当手术环境的复杂程度中等时，手术环境的复杂程度的数据特征标注为1，当手术环境简单时，手术环境的复杂程度的数据特征标注为0。以及，当患者的病症危急时，患者病症的危急程度的数据特征标注为2，当患者的病症危急程度中等时，患者病症的危急程度的数据特征标注为1，当患者的病症不危急时，患者病症的危急程度的数据特征标记为0。本领域技术人员可以通过对所有的历史手术的目标数据进行大数据分析后确定所述数据特征的级别划分标准，具体分析方法为本领域技术人员可以习知的内容，此次不做介绍。
所述步骤S12中，可以依据所述历史手术的术后评价来对所述历史手术的各所述功能性操作进行自动化等级划分。具体地，所述自动化等级可以划分为三个级别，分别为3级别、2级别和1级别，并且级别越低，允许所述手术操作装置30自动执行的动作种类越少，也即，3级别下允许所述手术操作装置30自动执行的动作类型最多，2级别下允许所述手术操作装置30自动执行的动作类型次之，1级别下允许所述手术操作装置30自动执行的动作类型最少。在对历史手术中的功能性操作进行自动化等级划分时，所述控制单元50首先将所有的历史手术的术后评价按照预定规则例如针对一指定的功能性操作的评价按照由好到坏的顺序进行排序，然后将排名为前30％的历史手术中的所述指定的功能性操作的自动化等级划分为3级，将排名为31％～70％的历史手术中的所述指定的功能性操作的自动化等级划分为2级，以及将排名为71％～100％的历史手术中的所述指定的功能性操作的自动化等级划分为1级。可以理解的是，若所述预定规则是针对所述指定的功能性操作的评价按照由坏到好的顺序对历史手术进行排序，那么可以将排名为前30％的历史手术中的所述指定的功能性操作的自动化等级划分为1级，将排名为31％～70％的历史手术中的所述指定的功能性操作的自动化等级划分为2级，以及将排名为71％～100％的历史手术中的指定的功能性操作的自动化等级划分为3级。
以每个功能性操作均具有四个所述目标数据，且四个所述目标数据分别为自动手术的操作难度、术中出血量、手术环境的复杂程度、以及患者病症的危急程度为例，当所述步骤S12执行完毕之后可以得到如下的表1，表1中将所述第一功能性操作编号为1，将所述第二功能性操作编号为2，将所述第三功能性操作编号为3。
表1
Figure PCTCN2022135879-appb-000001
由表1可以看到，同一个功能性操作在不同的手术中的自动化等级有所不同(例如第一功能性操作在一台历史手术中的自动化等级为3，在另一台历史手术中的自动化等级为2)。因此，所述控制单元50执行所述步骤S13，即通过自动化等级分类器的训练得到所述功能性操作的目标数据的数据特征与与之对应的自动化等级之间的映射关系。
为简明起见，本文仅以每一台历史手术中的第一功能性操作为例进行说明。表2中给出了十台历史手术中的所述第一功能性操作的目标数据的数据特征及每一台历史手术中的所述第一功能性操作的自动化等级。
表2
Figure PCTCN2022135879-appb-000002
所述步骤S13中，可以采用任意一种合适的分类算法来进行所述自动化等级分类器的训练，可选的算法包括但不限于决策树算法、朴素贝叶斯算法、逻辑回归算法、神经网络算法等。
本文中采用朴素贝叶斯算法进行所述分类器的训练。如此，所述第一功能性操作有K个自动化等级(本实施例中K为3)，并表达为y＝(y 1，y 2，……y K)，所述目标数据有D个类别(本实施例中D为4)，并表达为x＝(x 1，x 2，……x D)依据朴素贝叶斯算法，根据如下的公式(I)分别计算所述第一功能性操作的自动化等级的概率，即分别计算x属于y 1、y 2……y k的概率P(yk|X)，公式(I)为：
Figure PCTCN2022135879-appb-000003
其中，所有的所述目标数据之间相互独立，且P(X|yk)的计算公式为如下式(II)：
Figure PCTCN2022135879-appb-000004
以概率最大值所对应的自动化等级作为所述功能性操作的自动化等级，计算公式为如下式(III)：
y k＝arg max(P(y k|X))  y k∈Y         (III)。
根据式(I)、(II)、(III)对表2中的所有历史手术中的所述第一功能性操作进行计算，直至所述第一功能性操作的自动化等级分类器训练完成并存储。可以理解，所述控制单元50采用同样的方法可以得到其他的功能性操作的自动化等级分类器。
通过以上介绍可知，每一个所述分类器针对于一种功能性操作。因此，所述步骤S20实际是根据当前手术的各个功能性操作的目标数据及相应的自动化等级分类器得到当前手术的各个功能性操作的自动化等级，例如根据当前手术中的第一功能性操作的目标数据和第一功能性操作的自动化等级分类器得到当前手术的第一功能性操作的自动化等级、根据当前手术中的第二功能性操作的目标数据和第二功能性操作的自动化等级分类器得到当前手术的第二功能性操作的自动化等级、根据当前手术中的第三功能性操作的目标数据和第三功能性操作的自动化等级分类器得到当前手术的第三功能性操作的自动化等级等。
如此，如图8所示，所述步骤S20可具体包括如下步骤：
步骤S21：获取当前手术的手术数据。
步骤S22：从当前手术的手术数据中提取各所述功能性操作的所述目标数据，并获取所述目标数据的数据特征。以及，
步骤S23：将当前手术的各所述功能性操作的所述目标数据的数据特征输入相应的自动化等级分类器，并得到各所述功能性操作的对应的自动化等级。
如图9及图10所示，所示步骤S21中，当前手术的手术数据包括当前手术的术前数据和/或术中数据。其中术前数据包括患者信息、手术信息、自动手术信息、手术室信息，其中患者信息例如包括患 者的体征、患者病症的危急程度等信息，手术信息例如包括预估的手术时长、预估的术中出血量等信息，自动手术信息包括预估的自动手术操作难度等信息。术中数据即实时的手术数据，包括内窥镜采集的患者体内的图像信息、手术操作装置30的机械臂的运动信息、自动手术信息、术中出血量等信息。当前手术的手术信息可以采用任意合适的方式获取。本领域技术人员应当理解，利用术前的手术数据获取当前手术的自动化等级，实际上是在术前对某一功能性操作的自动化等级进行预测。
以及，所述步骤S22中的所述目标数据与所述步骤S11中的所述目标数据相同，也为自动手术的操作难度、术中出血量、手术环境的复杂程度、以及患者病症的危急程度中的至少一者。
请返回参考图8，所述步骤S22实际包括：
步骤S22a：对当前手术的手术数据进行清洗去噪，以去除非目标数据，并保留当前手术的所述功能性操作的所述目标数据。以及，
步骤S22b：通过大数据分析得到当前手术的所述目标数据的数据特征并存储。
在一种优选的实施方式中，当前手术的目标数据被分为两部分，分别为第一目标数据和第二目标数据，其中所述第一目标数据是指自动手术相关数据。所述自动手术相关数据可以由医护人员根据实际情况确定，在一些实施例中，当所述目标数据包括自动手术的操作难度、术中出血量、手术环境的复杂程度等时，所述自动手术相关数据包括自动手术的操作难度。
在此基础上，如图11所示，所述步骤S23可包括如下步骤：
步骤S23a：将当前手术的所述功能性操作的所有目标数据(即所述第一目标数据和所述第二目标数据)的数据特征输入相应的自动化分类器，并得到当前手术的所述功能性操作的第一自动化等级。
步骤S23b：将当前手术的所述功能性操作的所述第二目标数据的数据特征输入相应的自动化等级分类器，并得到当前手术的所述功能性操作的第二自动化等级。
步骤S23c：对所述第一自动化等级和所述第二自动化等级进行比较，并得到当前手术的所述功能性操作的自动化等级。
所述步骤S23c中，若所述第一自动化等级中允许所述手术操作装置30自动执行的手术动作的类型少于或等于所述第二自动化等级中允许所述手术操作装置30自动执行的手术动作的类型，则确定当前手术的所述功能性操作的自动化等级为所述第一自动化等级；若所述第一自动化等级中允许所述手术操作装置30自动执行的手术动作的类型比所述第二自动化等级中允许所述手术操作装置30自动执行的手术动作的类型多，则产生错误信息，以提示医护人员所述功能性操作不能在所述第一自动化等级下进行。这样做的目的是从多个维度考虑当前手术的所述功能性操作在自动执行时的安全性。
举例来说，假设当前手术中的第一功能性操作的四个目标数据的数据特征均为1，即自动手术的操作难度的数据特征为1，术中出血量的数据特征为1，手术环境的复杂程度为1，以及患者病症的危急程度也为1。根据如表2所示的历史手术数据训练得到所述第一功能性操作的自动化等级分类器，并据此计算当前手术中的第一功能性操作的自动化等级。过程如下：
首先，计算所述第一功能性操作在每一个自动化等级中出现的概率：
Figure PCTCN2022135879-appb-000005
Figure PCTCN2022135879-appb-000006
Figure PCTCN2022135879-appb-000007
Figure PCTCN2022135879-appb-000008
即，在当前手术中，所述第一功能性操作的第一自动化等级为1级别、2级别及3级别的概率分别为0.444、0.579、0，由此可以确定所述第一功能性操作的第一自动化等级为2级别。
接着，去除自动手术的操作难度这一目标数据，并计算所述第一功能性操作在每一个自动化等级中出现的概率：
Figure PCTCN2022135879-appb-000009
Figure PCTCN2022135879-appb-000010
Figure PCTCN2022135879-appb-000011
即，在当前手术中，所述第一功能性操作的第二自动化等级为1级别、2级别及3级别的概率分别为0.296、0.926、0，由此可以确定所述第一功能性操作的第二自动化等级为2级别。由此，当前手术中，所述第一功能性操作的目标自动化等级即为2级别。
所述步骤S30中，所述显示单元包括所述图像显示装置40和/或所述医生端控制装置10的沉浸式显示器，也即，如图12所示，通过所述图像显示装置40显示当前手术的功能性操作的自动化等级，或者例如所述医生端控制装置10的沉浸式显示器显示当前手术的功能性操作的自动化等级(图中未示出)。此外，所述显示单元上还显示其他信息，例如自动手术等级功能开关，在手术前或手术中，使所述自动手术等级功能开关开启，以使得所述控制单元50根据当前手术的手术数据进行相应的功能性操作的自动化等级的判断(根据术前的手术数据进行预判，或者根据术中的手术数据进行判断)。
本实施例中，在手术机器人系统执行自动手术之前，先执行手术等级确定方法，可以预先确定当前手术的目标自动化等级，从而所述手术操作装置可以在确定的目标自动化等级下执行相应的手术操作，提高自动手术的安全性和可靠性，避免对患者造成不必要的伤害。
<实施例二>
本实施例提供了一种手术等级确定装置，如图13所示，所述手术自动等级确定装置包括训练模块51和确定模块53，并还优选包括存储模块52，所述训练模块51用于训练所述手术的自动化等级分类器，所述存储模块52用于存储所述自动化等级分类器，所述确定模块53用于根据当前手术的手术数据以及所述自动化等级分类器得到当前手术的目标自动化等级。可选地，所述手术等级确定装置可集成于一控制机构，例如所述手术机器人系统的控制单元50中。
进一步地，所述训练模块51包括获取单元51a、划分单元51b和训练单元51c。
其中，所述获取单元51a用于从历史手术的手术数据中获取各功能性操作对应的目标数据，并获取所述目标数据的数据特征。
所述划分单元51b用于对所述历史手术中的各所述功能性操作对应的自动化等级进行划分。更详细地，所述划分单元51b用于根据术后评价对所述历史手术中的各个所述功能性操作对应的自动化等级进行划分。举例来说，可以首先对所述历史手术的术后评价按照预定规则进行排序，然后所述划分单元 51b再根据排序结构对各所述功能性操作对应的自动化等级进行划分。
所述训练单元51c用于根据所述历史手术中的各所述功能操作对应的所述目标数据的数据特征以及所述自动化等级划分结果，对相应的所述自动化等级分类器进行训练。在所述获取单元51a获取所述目标数据的数据特征，以及所述训练单元51c训练得到相应的自动化等级分类器之后，所述确定模块53根据输入相应的自动化等级分类器的所述当前手术中的所述功能性操作所对应的所述目标数据特征，得到所述当前手术的所述功能性操作的目标自动化等级。
可选地，所述目标数据包括第一目标数据和第二目标数据，所述目标数据具体可以包括对应手术的操作难度、术中出血量、手术环境的复杂程度、患者病症给你的危及程度等，其中，所述第一目标数据为自动手术相关数据，例如对应手术的操作难度。
在确定所述当前手术的所述功能性操作的目标自动化等级时，所述确定模块53首先用于根据输入相应的所述自动化等级分类器的当前手术的所述功能性操作的所有目标数据的数据特征，得到当前手术的所述功能性操作的第一自动化等级。然后根据输入相应的自动化等级分类器的所述第二目标数据，得到当前手术的所述功能性操作的第二自动化等级。最后对所述第一自动化等级和所述第二自动化等级进行比较，以得到所述当前手术的所述功能性操作的目标自动化等级。
此外，所述确定模块53还可以与所述显示单元(例如所述图像显示装置40和/或医生端控制装置10)及所述手术操作装置30通信连接，以在确定当前手术的目标自动化等级后，将所述目标自动化等级发送至所述显示单元进行显示，以及发送至所述手术操作装置30，这里，所述确定模块53可以先将所述目标自动化等级发送至所述手术操作装置30，以使所述手术操作装置30在确定的目标自动化等级下执行相应的手术操作。
不仅如此，所述获取单元51a还用于获取历史手术数据。与此同时，所述存储模块52与所述获取单元51a通信连接，以用于对所述历史手术的手术数据进行结构化存储。
<实施例三>
本实施例提供了一种手术机器人系统，所述手术机器人系统不仅包括如图1所示的控制端和执行端，还包括如图13所示的手术等级确定装置。所述手术等级确定装置可集成于所述手术机器人系统的控制单元50中。
需要说明的是，本申请实施例对所述控制单元50的具体设置方式不作限定，所述控制单元50可整体设置于所述医生端控制装置10处，或整体设置在所述患者端控制装置20处，或一部分地设置在所述医生端控制装置10处，一部分地设置在患者端控制装置20处，或者独立于所述患者端控制装置20及所述医生端控制装置10，只要其能够执行相应的功能即可。需要说明的是，当所述控制单元50独立于所述患者端控制装置20及所述医生端控制装置10之外时，所控制单元50还与所述医生端控制装置10、所述患者端控制装置20通信连接。
还需要说明的是，当前手术对于下一台手术来说也成为历史手术，因而在当前手术结束后，所述获取单元51a还用于获取当前手术的术后的手术数据，以更新所述历史手术的手术数据信息库(如图9及图10所示)，所述术后的手术数据包括术后评价、患者的术后恢复情况等。
另外，在所述手术机器人系统执行自动手术的过程中，可以通过所述语音提示系统播报开始自动手术或结束自动手术的信息，以及所述控制单元50或者所述医生端控制装置或者所述患者端控制装置还可以判断当前的自动手术操作是否安全以及相应的危险程度，并通过所述语音提示系统和/或所述蜂鸣器报警。
图14示出了所述手术机器人系统执行自动手术的流程图。在确定进行自动手术之后，如图14所示，所述自动手术的流程包括：
步骤S1：所述语音提示系统语音进行语音播报，以提示医护人员接下去将由所述手术操作装置30执行自动手术操作。语音播报的内容例如可以是“即将在某级别下执行自动手术”等。
步骤S2：所述手术操作装置30在确定的自动化等级的限制下执行自动手术操作。
步骤S3：实时判断当前的手术动作是否安全，若是，蜂鸣器不发出警报，若否，则执行步骤S4。
步骤S4：判断当前的手术动作的危险程度，若危险程度高，则所述蜂鸣器以第一频率发出第一警报，以提示介入操作，若危险程度低，则所述蜂鸣器以第二频率发出第二警报。
步骤S5：医护人员人工介入并判断是否要使用急停功能以退出自动手术，若否，则返回执行步骤S2，若是，则执行步骤S6。所述急停功能的启动开关可显示所述医生端控制装置10的沉浸式显示器上。
步骤S6：退出自动手术，且进行语音播报。
其中，所述步骤S3及所述步骤S4可以根据现有技术中的手术机器人系统的安全保护措施执行，例如步骤S3中通过判断所述手术器械60的运动速度是否超限以判断当前的手术动作是否安全，以及所述步骤S4中根据所述手术器械60的运动速度的超限程度判断当前手术动作的危险程度等。
此外，如图15所示，在执行自动手术的过程中，所述显示单元例如所述图像显示装置40还可以显示所述内窥镜所采集的患者体内的图像信息，以便于医护人员直观地观察当前的自动手术操作。
<实施例四>
本实施例提供了一种计算机可读存储介质，其上存储有程序，当所述程序被执行时，执行如实施例一所提供的手术等级确定方法。
其中，计算机可读存储接着可以包括但不限于便携式盘、硬盘、随机存储存储器、只读存储器、可擦拭可编程只读存储器、光存储器、磁存储器或上述的任意合适的组合。
<实施例五>
本实施例提供了一种电子设备，所述电子设备包括存储器和处理器,所述存储器上存储有可在所述处理器上运行的计算机程序，所述处理器用于执行所述计算机程序时实现如实施例一所提供的手术等级确定方法。
所述电子设备的组件包括但不限于至少一个所述存储器和至少一个所述处理器，还可以包括显示单元，以及连接这些组件的总线。
其中，所述总线包括数据总线、地址总线和控制总线。
所述存储器可包括易失性存储器，例如随机存取存储器(RAM)和/或高速缓存存储器，还可以进一步包括只读存储器(ROM)。所述存储器还可以包括具有一组(至少一个)程序模块的程序/实用工具，这样的程序模块包括但不限于操作系统、一个或多个应用程序、其他程序模块及程序数据，这些示例中的每一个或某种组合中可能包括网络环境的实现。
所述处理器通过运行存储在所述存储器上的计算机程序，从而执行各种功能应用以及数据处理，例如本申请实施例一所提供的手术等级确定方法。
此外，所述电子设备还可以与一个或多个外部设备例如键盘、指向设备等通信。这种通信可以通过输入/输出(I/O)接口进行。并且，所述电子设备还可以通过网络适配器与一个或多个网络例如局域网(LAN)、广域网(WAN)和/或公共网络进行通信。另外，结合所述电子设备还可以使用其他硬件和/或软件模块，包括但不限于微代码、设备驱动器、冗余处理器、外部磁盘驱动阵列、磁盘阵列系统、磁带驱动器以及数据备份存储系统等。
应当注意，尽管在上文详细描述中提及了所述电子设备的若干按压/模块或子单元/模块，但这种划分仅仅是示例性的而非强制性的。实际上，根据本申请的实施方式，上文描述的两个或更多个单元/模块的特征和功能可以在一个单元或模块中具体化。反之，上文描述的一个单元/模块的特征和功能可以进一步划分为由多个单元/模块来具体化。
虽然本申请披露如上，但并不局限于此。本领域的技术人员可以对本申请进行各种改动和变型而不脱离本申请的精神和范围。这样，倘若本申请的这些修改和变型属于本申请权利要求及其等同技术的范围之内，则本申请也意图包含这些改动和变型在内。

Claims (17)

  1. 一种手术等级确定方法，其特征在于，包括：
    根据历史手术的手术数据训练手术的自动化等级分类器；以及，
    根据当前手术的手术数据以及所述自动化等级分类器得到当前手术的目标自动化等级。
  2. 根据权利要求1所述的手术等级确定方法，其特征在于，每一台手术中执行至少一种功能性操作；
    所述根据历史手术的手术数据训练手术的自动化等级分类器包括：根据所述历史手术中的各功能性操作对应的目标数据训练相应功能性操作的自动化等级分类器；
    所述根据当前手术的手术数据以及所述自动化等级分类器得到当前手术的自动化等级包括：根据所述当前手术中的功能性操作所对应的目标数据以及相应的所述自动化等级分类器，得到所述当前手术执行的功能性操作的目标自动化等级。
  3. 根据权利要求2所述的手术等级确定方法，其特征在于，所述根据所述历史手术中的各功能性操作对应的目标数据训练相应功能性操作的自动化等级分类器的步骤包括：
    从所述历史手术的手术数据中提取各所述功能性操作对应的所述目标数据，并获取所述目标数据的数据特征；
    对所述历史手术中的各所述功能性操作对应的自动化等级进行划分，得到自动化等级划分结果；以及，
    根据所述历史手术中的各所述功能性操作对应的所述目标数据的数据特征以及所述自动化等级划分结果，对相应功能性操作对应的所述自动化等级分类器进行训练。
  4. 根据权利要求3所述的手术等级确定方法，其特征在于，对所述历史手术中的各所述功能性操作对应的自动化等级进行划分的步骤包括：根据术后评价对所述历史手术中的各所述功能性操作对应的自动化等级进行划分。
  5. 根据权利要求4所述的手术等级确定方法，其特征在于，所述根据术后评价对所述历史手术中的各所述功能性操作对应的自动化等级进行划分的步骤包括：将所述历史手术的术后评价按照预定规则进行排序，并按照排序结果对各所述功能性操作对应的自动化等级进行划分。
  6. 根据权利要求2所述的手术等级确定方法，其特征在于，所述根据当前手术中的功能性操作所对应的目标数据及相应的所述自动化等级分类器得到所述当前手术中的功能性操作的目标自动化等级的步骤包括：
    从所述当前手术的手术数据中提取所述功能性操作对应的所述目标数据，并获取所述目标数据的数据特征；以及，
    将所述当前手术中的所述功能性操作对应的所述目标数据的数据特征输入相应的所述自动化等级分类器，以得到所述当前手术的所述功能性操作的目标自动化等级。
  7. 根据权利要求6所述的手术等级确定方法，其特征在于，所述功能性操作的所述目标数据包括第一目标数据和第二目标数据；所述将当前手术中的所述功能性操作对应的所述目标数据的所述数据特征输入相应的所述自动化等级分类器，以得到所述功能性操作的目标自动化等级的步骤包括：
    将所述当前手术的所述功能性操作的所有目标数据的数据特征输入相应的所述自动化等级分类器，并得到当前手术的所述功能性操作的第一自动化等级；
    将所述第二目标数据的数据特征输入相应的所述自动化等级分类器，并得到所述当前手术的所述功能性操作的第二自动化等级；
    对所述第一自动化等级和所述第二自动化等级进行比较，得到所述当前手术的所述功能性操作的目标自动化等级；
    其中，所述第一目标数据为自动手术相关数据。
  8. 根据权利要求7所述的手术等级确定方法，其特征在于，所述对所述第一自动化等级和所述第二自动化等级进行比较，得到当前手术的所述功能性操作的目标自动化等级，包括：
    若所述第一自动化等级中允许自动执行的手术动作的类型少于或等于所述第二自动化等级中允许自动执行的手术动作的类型，则得到当前手术的所述功能性操作的目标自动化等级为所述第一自动化等级；若所述第一自动化等级中允许自动执行的手术动作的类型多于所述第二自动化等级中允许自动执行的手术动作的类型，则产生错误信息。
  9. 根据权利要求2所述的手术等级确定方法，其特征在于，所述目标数据包括对应手术的操作 难度、术中出血量、手术环境的复杂程度、以及患者病症的危急程度中的至少一种。
  10. 根据权利要求1所述的手术等级确定方法，其特征在于，所述手术等级确定方法还包括：使所述当前手术的目标自动化等级在一显示单元上进行显示或者通过语音方式进行提示。
  11. 根据权利要求1所述的手术等级确定方法，其特征在于，在所述根据历史手术的手术数据训练手术的自动化等级分类器的步骤之前，还包括：获取所述历史手术的手术数据，并对所述历史手术的手术数据进行结构化存储。
  12. 根据权利要求1所述的手术等级确定方法，其特征在于，在所述根据当前手术的手术数据以及所述自动化等级分类器得到当前手术的目标自动化等级之后，还包括：
    将所述目标自动化等级发送至手术操作装置，以使所述手术操作装置根据所述目标自动化等级执行相应的手术操作。
  13. 一种手术等级确定装置，其特征在于，包括：
    训练模块，用于根据历史手术的手术数据训练手术的自动化等级分类器；
    确定模块，用于根据当前手术的手术数据以及所述自动化等级分类器得到当前手术的目标自动化等级。
  14. 根据权利要求13所述的手术等级确定装置，其特征在于，所述训练模块包括：
    获取单元，用于从所述历史手术的手术数据中获取各功能性操作对应的目标数据，并获取所述目标数据的数据特征；
    划分单元，用于对所述历史手术中的各所述功能性操作对应的自动化等级进行划分，得到自动化等级划分结果；以及，
    训练单元，用于根据所述历史手术中的各所述功能性操作对应的所述目标数据的数据特征以及所述自动化等级划分结果，对相应的所述自动化等级分类器进行训练。
  15. 一种手术机器人系统，其特征在于，包括手术操作装置以及如权利要求13或14所述的手术等级确定装置，所述手术操作装置与所述手术等级确定装置通信连接，并用于根据所述当前手术的所述目标自动化等级执行相应的手术操作。
  16. 一种电子设备，包括存储器和处理器，所述存储器上存储有可在所述处理器上运行的计算机程序，所述处理器执行所述计算机程序时实现如权利要求1-12中任一项所述的手术等级确定方法。
  17. 一种计算机可读存储介质，其特征在于，所述计算机可读存储介质上存储有程序，当所述程序被执行时，执行如权利要求1-12中任一项所述的手术等级确定方法。
PCT/CN2022/135879 2021-12-01 2022-12-01 手术等级确定方法、装置、系统、设备及介质 WO2023098806A1 (zh)

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