WO2021127930A1 - Trauma ultrasonic detection method and system, mobile device, and storage medium - Google Patents

Abstract

A trauma ultrasonic diagnosis method and system, a mobile device, and a computer-readable storage medium. The method comprises: obtaining an electric signal converted from an ultrasonic echo and generating an ultrasonic image video stream, the ultrasonic echo being an ultrasonic echo received when an ultrasonic probe detects a detected person; displaying an ultrasonic video image; using an artificial intelligence model to analyze a detected part on the basis of the ultrasonic image video stream, and determine whether a fluid sonolucent area exists; and displaying an analysis result of the artificial intelligence model. The method improves the accuracy and efficiency of ultrasonic diagnosis, and avoids dependence on a mobile network.

Description

Trauma ultrasonic detection method, system, mobile equipment and storage medium Technical field

The present invention relates to the field of ultrasonic detection, in particular to a wound ultrasonic detection method, system, mobile equipment and storage medium.

Background technique

Medical ultrasound examination is a medical imaging diagnosis technology that uses ultrasound equipment to emit high-frequency sound waves and record the reflected waves generated by the tissue structure in the organism. Traditional desktop ultrasound equipment is usually bulky and heavy, and it is not convenient to move and transport. The use of desktop ultrasound equipment usually needs to be performed in the clinic. As the miniaturization and light weight of desktop ultrasound, portable ultrasound is similar to the size of a notebook computer, allowing ultrasound inspection to break away from the limitation that must be performed in a specific space. However, even if it is a portable ultrasound, its equipment weighs several kilograms, which is still a big challenge for long-distance and long-term carrying. Hand-held ultrasound is usually composed of a handheld ultrasound probe and a mobile device. The size that can be put into a pocket can meet scenarios with high portability requirements such as battlefield rescue or disease screening in remote areas. In addition, the affordable price of handheld ultrasound greatly reduces the difficulty of popularizing and promoting the use of ultrasound.

In addition, because skilled ultrasound diagnosis requires doctors to pay a long learning cycle, in some places, if there is no skilled ultrasound diagnosis doctor, it is impossible to use ultrasound equipment to complete ultrasound diagnosis. Therefore, artificial intelligence is used in ultrasound equipment to assist doctors in diagnosis and treatment. It is an effective solution that can shorten the learning curve for doctors and enable them to quickly get started with ultrasound diagnosis.

Limited by the limited computing power of hand-held ultrasound, artificial intelligence-enabled hand-held ultrasound inspection usually requires the deployment of smart models on a cloud server, and then data transmission through a network connection. However, the deployment of artificial intelligence models in the cloud is greatly limited by the quality of the network environment. Network bandwidth and network delay will directly affect the data transmission between handheld ultrasound and cloud servers, which may lead to the need for real-time display of intelligent analysis results. It is satisfied that when there is no network connection, the artificial intelligence analysis function of handheld ultrasound will be completely unusable due to the inability to transmit data.

Summary of the invention

In order to solve at least one of the above-mentioned problems and/or other potential problems in the prior art, it is necessary to provide a wound ultrasound diagnosis method, system, mobile device, and computer-readable storage medium.

In a first aspect, a wound ultrasound diagnosis method is provided, the method including:

Acquiring an electrical signal converted by an ultrasonic echo and generating an ultrasonic image video stream, the ultrasonic echo being the ultrasonic echo received when the ultrasonic probe detects the subject;

Display ultrasound video images;

Use an artificial intelligence model to analyze the detected part based on the ultrasound image video stream to determine whether there is a liquid dark area; and

The analysis result of the artificial intelligence model is displayed.

Further, the method further includes: if there is a liquid dark area, segmenting the liquid dark area using the artificial intelligence model, and analyzing the orientation, shape and/or size of the liquid dark area.

Further, the analysis result is that there is no liquid dark area or the distribution, shape and/or size of the liquid dark area.

Further, if there is a liquid dark area, the analysis result of the artificial intelligence model is displayed as: output the name of the part where the liquid dark area exists, the ultrasonic video image frame superimposed with the liquid dark area segmentation mask, and/or output The orientation, size and/or shape of each liquid dark zone.

Further, the artificial intelligence model uses a lightweight deep convolutional neural network as a model, and the trauma ultrasound diagnosis method is implemented using mobile devices.

Further, the "using an artificial intelligence model to analyze the detected part based on the ultrasound image video stream to determine whether there is a liquid dark area" includes: using a fully convolutional neural network to perform multiple convolutions on each frame of the ultrasound video image One or more feature images are obtained by calculation, and a probability is obtained after the one or more feature images are passed through the global pooling layer, the fully connected layer and the activation function, and whether there is a liquid dark zone is determined according to the probability.

Further, the "segmentation of the liquid dark area using the artificial intelligence model, and analyzing the position, shape and/or size of the liquid dark area" includes: using a fully convolutional neural network to perform ultrasound on each frame The video image is subjected to multiple convolution operations to obtain one or more feature images, and each feature image in the one or more feature images is then subjected to a 1*1 convolution operation to obtain a numerical matrix with the same size as the frame of the ultrasound video image Each value in the numerical matrix corresponds to a pixel of a frame of ultrasound video image, and the numerical matrix divides the numerical distribution of the corresponding liquid dark area through the activation function, and calculates each liquid according to the numerical distribution. The area and/or fluid volume of the dark zone.

Further, the method further includes: forming an artificial intelligence-assisted diagnosis and treatment report, and displaying and/or storing the artificial intelligence-assisted diagnosis and treatment report.

Further, the method further includes: using an artificial intelligence model to analyze which part of the diagnosed part is.

Further, the method further includes: using an artificial intelligence model to determine whether all diagnostic parts that need to be diagnosed have been diagnosed, and if all diagnostic parts that need to be diagnosed have been diagnosed, displaying the analysis results and/or formation of the artificial intelligence model In the artificial intelligence-assisted diagnosis and treatment report, if any diagnosis part that needs to be diagnosed has not been diagnosed, the electric signal converted by the ultrasonic echo is continued to be obtained.

In a second aspect, a trauma ultrasound diagnosis system is provided, and the system includes:

A signal acquisition module for acquiring an electrical signal converted by an ultrasonic echo, the ultrasonic echo being the ultrasonic echo received when the ultrasonic probe detects the subject;

A video image signal generating module, configured to generate an ultrasound image video stream according to the electrical signal;

Artificial intelligence model, the artificial intelligence model includes:

The liquid dark area judgment module is configured to receive the ultrasound image video stream, and analyze whether there is a liquid dark area in the detection part based on the ultrasound image video stream; and

The display module is used for receiving the ultrasound image video stream and displaying the ultrasound video image on a display unit, and for receiving the analysis result of the artificial intelligence model and displaying the analysis result on the display unit.

Further, the artificial intelligence model further includes: a liquid dark area segmentation module, the liquid dark area segmentation module is used to segment the liquid dark area, and analyze the orientation, shape and/or size of the liquid dark area .

Further, the analysis result of the artificial intelligence model is that there is no liquid dark zone or the distribution, shape and/or size of the liquid dark zone.

Further, the display module is used to output the name of the part where the liquid dark area exists, the ultrasonic video image frame superimposed with the liquid dark area segmentation mask, and/or output the orientation, size and/or of each liquid dark area Or shape.

Further, the artificial intelligence model adopts a lightweight deep convolutional neural network as a model, and the system is installed on a mobile device.

Further, the liquid dark area judgment module is used to perform multiple convolution operations on each frame of ultrasound video image using a fully convolutional neural network to obtain one or more feature images, and pass the one or more feature images through A probability is obtained after the global pooling layer, the fully connected layer, and the activation function, and it is determined whether there is a liquid dark zone according to the probability.

Further, the liquid dark area segmentation module is used to perform multiple convolution operations on each frame of ultrasound video image by using a fully convolutional neural network to obtain one or more feature images, and combine the one or more feature images Each feature image of the 1*1 convolution operation is performed to obtain a numerical matrix with the same size as the frame of the ultrasound video image, wherein each value in the numerical matrix corresponds to a pixel of the frame of the ultrasound video image, and the liquid The dark area segmentation module is also used to segment the numerical value distribution corresponding to the dark area of the liquid from the numerical matrix, and calculate the area and/or amount of liquid accumulation of each dark area of the liquid based on the value distribution.

Further, the system further includes an auxiliary diagnosis and treatment report generation module, and the auxiliary diagnosis and treatment report generation module is used to generate an artificial intelligence auxiliary diagnosis and treatment report according to the analysis result of the artificial intelligence model.

Further, the system further includes an auxiliary diagnosis and treatment report storage module, and the auxiliary diagnosis and treatment report storage module is used to store the artificial intelligence auxiliary diagnosis and treatment report.

Further, the display module is also used to display the artificial intelligence-assisted diagnosis and treatment report.

Further, the system further includes a diagnosis part judgment module, and the diagnosis part judgment module is used to judge which part the diagnosed part is.

Further, the system further includes a diagnosis completion judging module, the diagnosis completion judging module is used to judge whether all the diagnosed parts that need to be diagnosed have completed the diagnosis, and the auxiliary diagnosis and treatment report generation module is used to judge all When the diagnosis parts that need to be diagnosed have been diagnosed, the auxiliary diagnosis and treatment report is generated, and/or the display module is used to display the manual when the diagnosis completion judgment module judges that all the parts that need to be diagnosed have been diagnosed. The analysis result of the smart model is on the display unit.

In a third aspect, a mobile device is provided. The mobile device includes a communication unit, a display unit, a processing unit, and a storage unit. The storage unit stores a plurality of program modules, and the plurality of program modules are processed by the The unit loads and executes the above-mentioned trauma ultrasound diagnosis method.

In a fourth aspect, a computer-readable storage medium is provided, on which a computer program is stored, and the computer program is executed by a processing unit to realize the above-mentioned trauma ultrasound diagnosis method.

The trauma ultrasound diagnosis method, system, mobile device and storage medium provided by the embodiments of the present invention apply artificial intelligence-enabled medical treatment to mobile devices for ultrasound diagnosis of trauma, which can quickly and accurately perform ultrasound scans by medical staff , Real-time judgment of whether there is blood and fluid accumulation in the detected part of the subject, as well as its specific location and size. Therefore, with the assistance of artificial intelligence, even medical staff without extensive experience in ultrasound use can quickly interpret trauma ultrasound video images and make corresponding treatment measures. Therefore, the trauma ultrasound diagnosis method provided in this embodiment reduces the requirements for related skills of medical staff and greatly improves the accuracy and efficiency of ultrasound diagnosis. The application of artificial intelligence trauma ultrasound diagnosis and treatment to mobile devices can avoid dependence on mobile networks.

Description of the drawings

In order to explain the technical solutions of the embodiments of the present invention more clearly, the following will briefly introduce the drawings needed in the embodiments of the present invention. Obviously, the drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, without creative work, other drawings can be obtained from these drawings.

Fig. 1 is a schematic flowchart of a wound ultrasonic diagnosis method in the first embodiment of the present invention.

Figure 2 is a schematic diagram of an exemplary artificial intelligence analysis of whether there is a liquid dark zone.

Figure 3 is a schematic diagram of an exemplary artificial intelligence method for dividing liquid dark areas.

Fig. 4 is a schematic flowchart of a wound ultrasonic diagnosis method in the second embodiment of the present invention.

Fig. 5 is a schematic flow chart of a wound ultrasonic diagnosis method in the third embodiment of the present invention.

Fig. 6 is a system frame diagram of a trauma ultrasonic diagnosis system in the fourth embodiment of the present invention.

Figure 7 is a schematic diagram of the artificial intelligence module analyzing whether there is a liquid dark zone.

Figure 8 is a schematic diagram of the artificial intelligence module dividing the liquid dark area.

Fig. 9 is a system frame diagram of a trauma ultrasound diagnosis system in the fifth embodiment of the present invention.

Fig. 10 is a system frame diagram of a trauma ultrasonic diagnosis system in the sixth embodiment of the present invention.

Fig. 11 is a system frame diagram of a trauma ultrasonic diagnosis system in the seventh embodiment of the present invention.

Fig. 12 is a system frame diagram of a trauma ultrasonic diagnosis system in the eighth embodiment of the present invention.

The following specific embodiments will further illustrate the present invention in conjunction with the above-mentioned drawings.

Symbol description of main components

Step S11-S17, S21-S28, S31-S39

Trauma ultrasound diagnosis system 60, 90, 100, 110

Signal acquisition module 61

Video image signal generation module 62

Artificial intelligence model 63, 101, 111, 126

Display module 64, 95

Ultrasound probe 70, 130

Liquid dark area judgment module 631

Liquid dark zone segmentation module 633

Display unit 80

Auxiliary diagnosis and treatment report generation module 91, 113

Auxiliary diagnosis and treatment report storage module 93

Storage unit 82

Cloud server 84

Diagnosis part judgment module 102

Diagnosis completion judgment module 112

Mobile equipment 120

Processing unit 121

Storage unit 122

Communication unit 123

Display unit 124

Ultrasonic testing procedures 125

Detailed ways

The technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative work shall fall within the protection scope of the present invention.

It should be noted that when a component is referred to as being "fixed to" or "installed on" another component, it can be directly on the other component or a central component may also exist. When a component is considered to be "installed on" another component, it can be directly installed on another component or a centered component may exist at the same time. The term "and/or" as used herein includes all and any combinations of one or more related listed items.

Example one

Please refer to FIG. 1, which is a schematic flowchart of a wound ultrasonic diagnosis method in Embodiment 1 of the present invention. According to different needs, the order of the steps in the flowchart can be changed, and some steps can be omitted. For ease of description, only the parts related to the embodiment of the present invention are shown.

As shown in Fig. 1, the wound ultrasound diagnosis method in the first embodiment includes the following steps.

Step S11: Obtain the electrical signal corresponding to the ultrasound echo sent by the ultrasound probe at the mobile device side, and generate an ultrasound image video stream.

In this embodiment, a communication connection is established between the mobile device and a hand-held ultrasound probe, and the hand-held ultrasound probe is used to obtain the ultrasound echo of the detected part of the examinee by drawing a picture on the detected part of the examinee. The ultrasonic echo is converted into an electrical signal by the handheld ultrasonic device and sent to the mobile device, so that the mobile device can continuously obtain the electrical signal corresponding to the ultrasonic echo sent by the ultrasonic probe and generate an ultrasonic image video stream.

Step S12: Display the ultrasound video image on the mobile device.

Step S13: Use the artificial intelligence model to analyze the detected part based on the ultrasound image video stream to determine whether there is a liquid dark area of effusion or blood accumulation. If there is a liquid dark area, go to step S14, if there is no liquid dark area Area, go to step S15.

Step S14: Use the artificial intelligence model to segment the liquid dark area, and analyze the orientation, shape and/or size of the liquid dark area.

In this embodiment, step S12 is not necessarily related to steps S13 and S14, and step S12 can be performed simultaneously with steps S13 and S14, or performed before or after steps S13 and S14.

In this embodiment, the artificial intelligence model uses a lightweight deep convolutional neural network as the model, and uses the convolutional layer in the convolutional neural network to filter each frame of the input ultrasound image video stream through the convolution kernel To extract feature information. The artificial intelligence model includes multiple convolutional layers, and the convolutional layers are connected to each other, and each frame of ultrasound video image is extracted level by level with richer semantic information and more distinguishable features, so as to facilitate the comparison of the results. Make accurate predictions. Each convolutional layer contains many parameters and requires a lot of calculations to extract features. In order to successfully deploy the artificial intelligence model on mobile devices, in this embodiment, the convolutional layer is optimized to achieve acceptable accuracy. Within the scope, the amount of parameters and calculations are reduced as much as possible, so that the artificial intelligence model can achieve performance optimization when computing resources are limited. The optimization of the convolutional layer can refer to the "mobilenet" lightweight network proposed by Google.

Please refer to Figure 2 and Figure 3. In this embodiment, the artificial intelligence model analysis is divided into two stages. In the first stage, the full convolutional neural network 1 is used to analyze whether there is a dark area of effusion. After the stream is output to the artificial intelligence model, the fully convolutional neural network 1 passes the ultrasound video image of each frame through multiple convolution operations of multiple convolution layers to obtain one or more feature images, the one or Multiple feature images are highly refined after the global pooling layer, and their dimensions are reduced, and then the fully connected layer is used for feature classification and synthesis. Finally, the output of the fully connected layer is connected to the activation function 1. The activation function 1 can be " Softmax" function or "Sigmoid" function, the activation function 1 compresses the value of the feature tensor output by the fully connected layer to a range of 0 to 1. The value or array obtained through activation function 1 is a probability. In this embodiment, the probability is less than 0.5 and it is considered that there is no liquid dark zone. The artificial intelligence model returns to the judgment result of the liquid-free dark zone after step S13. If the probability is greater than or equal to 0.5, it is considered that there is a liquid dark zone, and the artificial intelligence model returns the judgment result of the existence of a liquid dark zone after step S13. In other embodiments, the probability limit for setting the presence or absence of a liquid dark zone can be any value between 0 and 1 according to specific conditions and different neural network models and parameters used. In the second stage, the full convolutional network 2 is used to segment and measure the existing liquid dark areas. The fully convolutional neural network 2 passes the ultrasound video image of each frame through multiple convolution operations of multiple convolutional layers to obtain one or more feature images, and each feature in the one or more feature images The image is then subjected to a convolution operation with a 1*1 convolution layer to obtain a numerical matrix with the same size as the original ultrasound video image frame. Each value in the numerical matrix represents a corresponding pixel point in the original ultrasound video image frame. The activation The function compresses each value in the numeric matrix to the range of 0 to 1. The size of each value indicates the probability that the pixel in the corresponding ultrasound image belongs to the liquid dark zone. In this embodiment, it is greater than Equal to 0.5 indicates that the pixel belongs to the liquid dark area, and less than 0.5 indicates that the pixel does not belong to the liquid dark area. The activation function then divides the corresponding liquid dark area in the numeric matrix according to the magnitude of each value in the numeric matrix. The distribution of values. Further, the activation function combines the spatial scale corresponding to each pixel of the ultrasound probe to calculate the area and/or the amount of fluid accumulation of each liquid dark zone. In other embodiments, depending on the specific situation and the different neural network models and parameters used, the probability limit for judging whether the pixel corresponding to each value in the numerical matrix belongs to the liquid dark zone can be any value between 0 and 1 .

In another embodiment, in the second stage, the results of multiple convolution operations in which each frame of the ultrasound video image in the first stage is passed through multiple convolution layers can be used in the second stage, and the liquid dark area can be divided and calculated on this result. The orientation, shape and/or size of the liquid dark zone.

Step S15: Display the analysis result of the artificial intelligence model.

In this embodiment, the analysis result of the artificial intelligence model is displayed on the mobile terminal. According to different situations, the analysis result of the artificial intelligence model may be "there is no liquid dark zone" or the distribution of the liquid dark zone , Shape and/or size. When the analysis result of the artificial intelligence model is the distribution, shape and/or size of the liquid dark area, the display can be in any one of the following ways or a combination of two or more of the following ways: output the existence product The name of the liquid part, the ultrasonic video image frame superimposed with the liquid dark area segmentation mask, and the size and/or shape of each piece of effusion are output.

Step S16, forming an artificial intelligence-assisted diagnosis and treatment report.

Step S17: Display and/or store the artificial intelligence-assisted diagnosis and treatment report.

In this embodiment, the artificial intelligence-assisted diagnosis and treatment report may be stored in a mobile device, or the artificial intelligence-assisted diagnosis and treatment report may also be uploaded and saved to a cloud server.

The trauma ultrasound diagnosis method provided in this embodiment applies artificial intelligence-enabled medical treatment to mobile devices for ultrasound diagnosis of trauma, which can quickly and accurately determine whether the detected part of the subject is detected in real time during the ultrasound scan performed by medical staff. There are blood and fluid accumulation and their specific location and size. Therefore, with the assistance of artificial intelligence, even medical staff without extensive experience in ultrasound use can quickly interpret trauma ultrasound video images and make corresponding treatment measures. Therefore, the trauma ultrasound diagnosis method provided in this embodiment reduces the requirements for related skills of medical staff and greatly improves the accuracy and efficiency of ultrasound diagnosis. The application of artificial intelligence trauma ultrasound diagnosis and treatment to mobile devices can avoid dependence on mobile networks.

In another embodiment, the wound ultrasound diagnosis method may include steps S11-S15, but does not include S16 and S17.

In another embodiment, the ultrasonic diagnosis method for trauma may further include the step of establishing a communication connection between the mobile device and the ultrasound probe, and after the communication connection between the mobile device and the ultrasound probe is established, step S11 is started.

Example two

Please refer to FIG. 4, which is a schematic flowchart of a wound ultrasonic diagnosis method in Embodiment 2 of the present invention. According to different needs, the order of the steps in the flowchart can be changed, and some steps can be omitted. For ease of description, only the parts related to the embodiment of the present invention are shown.

As shown in Figure 4, the wound ultrasound diagnosis method in the second embodiment includes the following steps.

Step S21: Obtain the electrical signal corresponding to the ultrasonic echo sent by the ultrasonic probe at the mobile device side, and generate an ultrasonic image video stream.

Step S22: Display the ultrasound video image on the mobile device.

Steps S21, S22 and their related descriptions in this embodiment are respectively consistent with steps S11, S12 and their related descriptions in Embodiment 1. For details, please refer to Embodiment 1, which will not be repeated here.

Step S23: Use the artificial intelligence model to analyze which part of the diagnosed part is.

In this embodiment, the part that can be diagnosed by the artificial intelligence model can be around the liver, spleen, pericardium, pelvic cavity or lung. The artificial intelligence model can judge which part of the diagnosis is based on the user's input, for example, the user selects via a mobile device For the part to be diagnosed, the artificial intelligence model judges which part is diagnosed according to the user's choice. The artificial intelligence model can also judge which part is diagnosed based on the ultrasound image video stream and according to different parameters or pictures of different parts.

Step S24: Use the artificial intelligence model to analyze the diagnosed part based on the ultrasound image video stream to determine whether there is a liquid dark area of effusion or blood accumulation, if there is a liquid dark area, go to step S25, if there is no liquid dark area Area, go to step S26.

Step S25: Use the artificial intelligence model to segment the liquid dark area, and analyze the orientation, shape and/or size of the liquid dark area.

Step S26: Display the analysis result of the artificial intelligence model on the mobile terminal.

Step S27: Form an artificial intelligence-assisted diagnosis and treatment report.

Step S28: Display and/or store the artificial intelligence-assisted diagnosis and treatment report.

Steps S24-S28 and related descriptions in this embodiment are respectively consistent with steps S13-S17 and related descriptions in Embodiment 1. For details, please refer to Embodiment 1, and will not be repeated here.

In this embodiment, step S22 and steps S23-S25 are not necessarily sequential. Step S22 can be performed simultaneously with steps S23-S25, or performed before or after steps S23-S25.

The trauma ultrasound diagnosis method provided in this embodiment applies artificial intelligence-enabled medical treatment to mobile devices for ultrasound diagnosis of trauma, which can quickly and accurately determine the location of medical staff performing ultrasound scanning, and determine whether there is blood accumulation in the detected location in real time. Fluid effusion and its specific location and size. Therefore, with the assistance of artificial intelligence, even medical staff without extensive experience in ultrasound use can quickly interpret trauma ultrasound video images and make corresponding treatment measures. Therefore, the trauma ultrasound diagnosis method provided in this embodiment reduces the requirements for related skills of medical staff and greatly improves the accuracy and efficiency of ultrasound diagnosis. The application of artificial intelligence trauma ultrasound diagnosis and treatment to mobile devices can avoid dependence on mobile networks.

Example three

Please refer to FIG. 5, which is a schematic flowchart of a wound ultrasound diagnosis method in Embodiment 3 of the present invention. According to different needs, the order of the steps in the flowchart can be changed, and some steps can be omitted. For ease of description, only the parts related to the embodiment of the present invention are shown.

As shown in Fig. 5, the wound ultrasound diagnosis method in the third embodiment includes the following steps.

Step S31: Obtain the electrical signal corresponding to the ultrasound echo sent by the ultrasound probe at the mobile device side, and generate an ultrasound image video stream.

Step S32: Display the ultrasound video image on the mobile device.

Step S33: Use the artificial intelligence model to analyze which part of the diagnosed part is.

Step S34: Use an artificial intelligence model to analyze the diagnosed part based on the ultrasound image video stream to determine whether there is a liquid dark area of effusion or blood accumulation, if there is a liquid dark area, go to step S35, if there is no liquid dark area Area, go to step S36.

Step S35: Use the artificial intelligence model to segment the liquid dark area, and analyze the orientation, shape and/or size of the liquid dark area.

Steps S31-S35 and related descriptions in this embodiment are respectively consistent with steps S21-S25 and related descriptions in the second embodiment. For details, please refer to the second embodiment, which will not be repeated here.

In this embodiment, step S32 and steps S33-S35 are not necessarily sequential. Step S32 can be performed simultaneously with steps S33-S35, or performed before or after steps S33-S35.

Step S36: Display the analysis result of the artificial intelligence model on the mobile terminal.

Step S37: Use the artificial intelligence model to determine whether all the diagnostic parts that need to be diagnosed have been diagnosed, if yes, go to step S38, if not, go to step S31 to start ultrasound diagnosis of the next part.

Step S38, forming an artificial intelligence-assisted diagnosis and treatment report.

Step S39: Display and/or store the artificial intelligence-assisted diagnosis and treatment report.

Steps S37-S39 and related descriptions in this embodiment are respectively consistent with steps S26-S28 and related descriptions in the second embodiment. For details, please refer to the second embodiment, which will not be repeated here.

It can be understood that, in another embodiment, step S37 may be performed before step S36. If step S37 determines that all diagnostic parts that need to be diagnosed have been diagnosed, the flow proceeds to step S36, and after step S36 is completed, it proceeds to step S38. Alternatively, step S38 is executed at the same time as step S36 is executed. If it is determined in step S37 that there are still parts to be diagnosed that have not been diagnosed, the flow proceeds to step S31 to start ultrasound diagnosis of the next part.

The trauma ultrasound diagnosis method provided in this embodiment applies artificial intelligence-enabled medical treatment to mobile devices for ultrasound diagnosis of trauma, which can quickly and accurately assist medical staff in performing ultrasound diagnosis of all parts to be inspected, and judge whether there is any accumulation in the detected part in real time. Blood, effusion and its specific location and size. Therefore, with the assistance of artificial intelligence, even medical staff without extensive experience in ultrasound use can quickly interpret trauma ultrasound video images and quickly complete trauma ultrasound diagnosis of all parts to be inspected. Therefore, the trauma ultrasound diagnosis method provided in this embodiment reduces the requirements for related skills of medical staff and greatly improves the accuracy and efficiency of ultrasound diagnosis. The application of artificial intelligence trauma ultrasound diagnosis and treatment to mobile devices can avoid dependence on mobile networks.

Figures 1-5 illustrate the trauma ultrasound diagnosis method in different embodiments of the present invention. The use of artificial intelligence to diagnose ultrasound video images reduces the requirements for the relevant skills of medical staff and greatly improves the accuracy and accuracy of ultrasound diagnosis of trauma. effectiveness. The functional modules and hardware device architecture of the software system implementing the trauma ultrasonic diagnosis method will be introduced below in conjunction with FIG. 6. It should be understood that the embodiments are only for illustrative purposes, and are not limited by this structure in the scope of the patent application.

Example four

Please refer to FIG. 6, which is a system frame diagram of the trauma ultrasound diagnosis system in the fourth embodiment of the present invention. In this embodiment, the trauma ultrasound diagnosis system may include multiple functional modules composed of program code segments. The program code of each program segment of the trauma ultrasound diagnosis system can be stored in a memory of a computer device, such as a mobile device, and executed by at least one processor in the computer device, so as to realize a trauma ultrasound diagnosis function.

In this embodiment, the trauma ultrasound diagnosis system 60 can be divided into multiple functional modules according to the functions it performs, and each functional module is used to execute each of the corresponding embodiments in FIG. 1, FIG. 3, or FIG. 4. Steps to achieve the function of ultrasound diagnosis of trauma. In this embodiment, the functional modules of the trauma ultrasound diagnosis system 60 include: a signal acquisition module 61, a video image signal generation module 62, an artificial intelligence model 63, and a display module 64. The functions of each functional module will be described in detail in the following embodiments.

The signal acquisition module 61 is used to acquire the electrical signal corresponding to the ultrasonic echo sent by the ultrasonic probe 70.

The video image signal generating module 62 is configured to generate an ultrasound image video stream according to the electrical signal.

The artificial intelligence model 63 includes a liquid dark area judgment module 631 and a liquid dark area segmentation module 633. The liquid dark area judging module 631 is configured to receive the ultrasound image video stream, and analyze and detect whether there is a liquid dark area with effusion or hemorrhage based on the ultrasound image video stream. The liquid dark area segmentation module 633 is used to segment the liquid dark area and analyze the orientation, shape and/or size of the liquid dark area.

In this embodiment, the artificial intelligence model uses a lightweight deep convolutional neural network as the model, and uses the convolutional layer in the convolutional neural network to filter each frame of the input ultrasound image video stream through the convolution kernel To extract feature information. The artificial intelligence model includes multiple convolutional layers, and the convolutional layers are connected to each other, and each frame of ultrasound video image is extracted level by level with richer semantic information and more distinguishable features, so as to facilitate the comparison of the results. Make accurate predictions. Each convolutional layer contains many parameters and requires a lot of calculations to extract features. In order to successfully deploy the artificial intelligence model on mobile devices, in this embodiment, the convolutional layer is optimized to achieve acceptable accuracy. Within the scope, the parameter amount and calculation amount are reduced as much as possible, so that the artificial intelligence model achieves performance optimization in the case of limited computing resources. The optimization of the convolutional layer can refer to the "mobilenet" lightweight network proposed by Google.

Referring to Figures 7 and 8, in this embodiment, the liquid dark area determination module 631 uses a fully convolutional neural network 1 to analyze whether there is a dark area of effusion. After the ultrasound image video stream is output to the artificial intelligence model, The fully convolutional neural network 1 passes the ultrasound video image of each frame through multiple convolution operations of multiple convolutional layers to obtain one or more feature images, and the multiple feature images are passed through the global pooling layer. The features are highly purified and the dimensions are reduced, and then the fully connected layer is used for feature classification and synthesis. Finally, the output of the fully connected layer is connected to the activation function 1. The activation function 1 can be a "Softmax" function or a "Sigmoid" function. The activation function 1 compresses the value of the feature tensor output by the fully connected layer to the range of 0 to 1. The value or array obtained through the activation function 1 is a probability. In this embodiment, the probability is less than 0.5 and it is considered that there is no dark liquid zone. The dark liquid zone judgment module 631 outputs the judgment result of the dark zone without liquid. If the probability is greater than or equal to 0.5, it is considered that there is a liquid dark zone, and the liquid dark zone judgment module 631 outputs the judgment result of the existence of a liquid dark zone. In other embodiments, the probability limit for setting the presence or absence of a liquid dark zone can be any value between 0 and 1 according to specific conditions and different neural network models and parameters used. The liquid dark area segmentation module 633 uses the fully convolutional network 2 to segment and measure the liquid dark area when the liquid dark area judging module 631 determines that there is a liquid dark area. The fully convolutional neural network 2 passes the ultrasound video image of each frame through multiple convolution operations of multiple convolutional layers to obtain one or more feature images, and each feature in the one or more feature images The image is then subjected to a convolution operation with a 1*1 convolution layer to obtain a numerical matrix with the same size as the original ultrasound video image frame. Each value in the numerical matrix represents a corresponding pixel point in the original ultrasound video image frame. The activation The function compresses each value in the numeric matrix to the range of 0 to 1. The size of each value indicates the probability that the pixel in the corresponding ultrasound image belongs to the liquid dark zone. In this embodiment, it is greater than Equal to 0.5 indicates that the pixel belongs to the liquid dark area, and less than 0.5 indicates that the pixel does not belong to the liquid dark area. The activation function then divides the value of the corresponding liquid dark area in the numeric matrix according to each value in the numeric matrix. distributed. Further, the activation function combines the spatial scale corresponding to each pixel of the handheld ultrasound probe to calculate the area and/or fluid accumulation of each liquid dark zone. In other embodiments, depending on the specific situation and the different neural network models and parameters used, the probability limit for judging whether the pixel corresponding to each value in the numerical matrix belongs to the liquid dark zone can be any value between 0 and 1 .

In another embodiment, the liquid dark area segmentation module 633 can use the liquid dark area judgment module 631 to pass the ultrasonic video image of each frame through multiple convolutional layers to the results of multiple convolution operations, where the result is Divide the liquid dark area up and calculate the position, shape and/or size of the liquid dark area.

The display module 64 is used for receiving the ultrasound image video stream and displaying the ultrasound video image on a display unit 80, and for receiving the analysis result of the artificial intelligence model 63 and displaying the analysis result on the display On unit 80.

In this embodiment, the analysis result of the artificial intelligence model 63 may be "there is no liquid dark zone" or the distribution, shape and/or size of the liquid dark zone. When the analysis result of the artificial intelligence model is the distribution, shape and/or size of the liquid dark area, the display module 64 may display the Analysis result: output the name of the part where the effusion exists, the ultrasonic video image frame superimposed with the liquid dark area segmentation mask, and output the size and/or shape of each effusion.

The trauma ultrasound diagnosis system 60 provided in this embodiment applies artificial intelligence-enabled medical treatment to trauma ultrasound diagnosis, and can quickly and accurately determine whether there is blood accumulation in the detected part of the subject in the process of ultrasonic scanning by medical staff. , Fluid effusion and its specific location and size. Therefore, with the assistance of artificial intelligence, even medical staff without extensive ultrasound experience can quickly interpret trauma ultrasound video images and make corresponding treatment measures. Therefore, the trauma ultrasound diagnosis method provided in this embodiment reduces the requirements for related skills of medical staff and greatly improves the accuracy and efficiency of ultrasound diagnosis. The application of the trauma ultrasound diagnosis system 60 to mobile devices can avoid dependence on the mobile network.

Example five

Please refer to FIG. 9, which is a system frame diagram of the trauma ultrasound diagnosis system in the fifth embodiment of the present invention. Different from the trauma ultrasound diagnosis system 60 in the fourth embodiment, the trauma ultrasound diagnosis system 90 of the fifth embodiment further includes an auxiliary diagnosis and treatment report generation module 91 and an auxiliary diagnosis and treatment report storage module 93. The auxiliary diagnosis and treatment report generation module 91 is used to generate an auxiliary diagnosis and treatment report according to The analysis result of the artificial intelligence model 63 generates an artificial intelligence-assisted diagnosis and treatment report. The auxiliary diagnosis and treatment report storage module 93 is also used to store the artificial intelligence auxiliary diagnosis and treatment report. In this embodiment, the auxiliary diagnosis and treatment report storage module 93 may store the artificial intelligence auxiliary diagnosis and treatment report in the storage unit 82 of the mobile device, or upload and save the artificial intelligence auxiliary diagnosis and treatment report to a cloud server. 84.

Different from the display module 64 in the fourth embodiment, in addition to displaying the ultrasound video image and the analysis result of the artificial intelligence model 63, the display module 95 in this embodiment is also used to display the artificial intelligence-assisted diagnosis and treatment report.

The beneficial effects of this embodiment can refer to the beneficial effects of the fourth embodiment. In addition to the beneficial effects of the fourth embodiment, the trauma ultrasound diagnosis system 90 of this embodiment generates and stores artificial intelligence-assisted diagnosis and treatment reports, which can prepare diagnosis and treatment reports. Repeated use and subsequent further verification of the artificial intelligence model.

Example Six

Please refer to FIG. 10, which is a system frame diagram of the trauma ultrasound diagnosis system in the sixth embodiment of the present invention. Different from the trauma ultrasound diagnosis system 90 in the fifth embodiment, the artificial intelligence model 101 of the trauma ultrasound diagnosis system 100 in the sixth embodiment further includes a diagnosis part judgment module 102, which is used to analyze which part is diagnosed. One part. In this embodiment, the part that can be diagnosed by the artificial intelligence model 101 may be around the liver, around the spleen, pericardium, around the pelvis, or lung, and the diagnostic part judgment module 102 can judge which part of the diagnosis is according to the input of the user. For example, a user selects a part to be diagnosed through a computer device, the diagnostic part judgment module 102 judges which part to be diagnosed according to the user's selection, the diagnostic part judgment module 102 may also be based on the ultrasound image video stream, according to different parts Different parameters or pictures to determine which part of the diagnosis is.

The beneficial effects of this embodiment can refer to the beneficial effects of the fifth embodiment. In addition to the same beneficial effects of the fifth embodiment, the trauma ultrasound diagnosis system 100 of this embodiment uses the artificial intelligence model 101 to determine which part of the diagnosed part is. It can further improve the accuracy of diagnosis.

Example Seven

Please refer to FIG. 11, which is a system frame diagram of the trauma ultrasonic diagnosis system in the seventh embodiment of the present invention. Different from the trauma ultrasound diagnosis system 100 in the sixth embodiment, the artificial intelligence model 111 of the trauma ultrasound diagnosis system 110 in the seventh embodiment also includes a diagnosis completion judgment module 112, which is used to judge all diagnoses that need to be diagnosed. Whether the location is complete diagnosis. Different from the sixth embodiment, the auxiliary diagnosis and treatment report generating module 113 in this embodiment is used to generate an auxiliary diagnosis and treatment report when the diagnosis completion judgment module 112 judges that all diagnosis parts that need to be diagnosed have been diagnosed. Or, in another embodiment, different from the sixth embodiment, the display module 64 is configured to display the analysis result of the artificial intelligence model when the diagnosis completion judgment module judges that all diagnosis parts that need to be diagnosed have been diagnosed. Displayed on the display unit 80.

The beneficial effects of this embodiment can be referred to the beneficial effects of the sixth embodiment. In addition to the same beneficial effects of the sixth embodiment, the trauma ultrasound diagnosis system 110 of this embodiment uses the artificial intelligence model 111 to determine whether the diagnosis of all parts to be diagnosed is completed. It can avoid missing the detection of key parts.

Example eight

FIG. 12 is a schematic diagram of functional modules of a mobile device in the eighth embodiment of the present invention. The mobile device 120 includes a processing unit 121, a storage unit 122, a communication unit 123, a display unit 124, and a built-in ultrasonic testing program 125 and an artificial intelligence model 126. The ultrasonic testing program 125 and the artificial intelligence model 126 can be installed on the mobile device 120 in the form of an application program, and the processing unit, the storage unit 122, the communication unit 123 and the display unit 124 of the mobile device 120 are used to complete the ultrasonic diagnosis of trauma.

In this embodiment, the mobile device 120 may be, but is not limited to, a smart phone, a tablet computer, etc. In other embodiments, an electronic device for ultrasonic diagnosis of trauma is completed by installing the ultrasonic detection program 125 and the artificial intelligence model 126 It is not limited to the mobile device 120, and may also be other terminal devices with computing operation capabilities, such as desktop computers.

The communication unit 123 is used to communicate with an ultrasonic probe 70, obtain the electrical signal corresponding to the ultrasonic echo sent by the ultrasonic probe 70 from the ultrasonic probe 70, and transmit the electrical signal to the ultrasonic testing program 125 uses. The communication unit 123 and the ultrasound probe 70 may be connected via a wired connection, using a wired communication technology, or may be a wireless connection, using a wireless communication technology. The communication unit 123 may be a signal transceiving unit matching the corresponding communication mode.

The storage unit 122 is used to store the ultrasonic testing program 125 and the artificial intelligence model 126 and the data (such as parameters) they need to use or the data generated (such as analysis results, diagnosis reports, etc.).

The processing unit 121 is used to execute the ultrasonic detection program 125 and the artificial intelligence model 126 to complete the ultrasonic diagnosis of trauma. When the processing unit 121 executes the ultrasonic detection program 125 and the artificial intelligence model 126, the steps of the trauma ultrasonic diagnosis method in the above method embodiment are implemented, or the processing unit 121 executes the ultrasonic detection program 125 and the artificial intelligence model 126 The function of each module in the trauma ultrasound diagnosis system in the above-mentioned embodiment is realized.

Exemplarily, both the ultrasonic testing program 125 and the artificial intelligence model 126 may be divided into one or more modules, and the one or more modules are stored in the storage unit 122 and processed by the The unit 121 executes to complete various embodiments of the present invention. The one or more modules/units may be a series of computer program instruction segments that can complete specific functions, and the instruction segments are used to describe the ultrasonic testing program 125 or the artificial intelligence model 126 in the electronic device 10 Implementation process. For example, the ultrasonic testing program 125 can be divided into modules 61, 62 and 64 in the fourth embodiment and FIG. 6, and the artificial intelligence model 126 can be divided into modules 631 and 632 in the fourth embodiment and FIG. 6; Alternatively, the ultrasonic testing program 125 can be divided into the modules 61, 62, 91, 93 and 95 in the fifth embodiment and FIG. 9, and the artificial intelligence model 126 can be divided into the modules 631 and 632 in the fourth embodiment. Or, the ultrasonic testing program 125 can be divided into modules 61, 62, 91, 93 and 95 in the sixth embodiment and FIG. 10, and the artificial intelligence model 126 can be divided into the sixth embodiment and the modules in FIG. 10 Modules 631, 632, and 102; or, the ultrasonic testing program 125 can also be divided into the seventh embodiment and the modules 61, 62, 91, 93, and 95 in FIG. 11, and the artificial intelligence model 126 can also be divided into The seventh embodiment and the modules 631, 632, 102, and 112 in FIG. 11.

Those skilled in the art can understand that the schematic diagram 12 is only an example of the electronic device 10, and does not constitute a limitation on the mobile device 120. The mobile device 120 may include more or less components than those shown in the figure, or combine certain components. , Or different components, for example, the mobile device 120 may also include input and output devices.

The so-called processing unit 121 can be any type of processor that can run the artificial intelligence model 126, such as a digital signal processor (DSP), an application specific integrated circuit (ASIC), etc. The processing unit 121 uses various types of processors. Such interfaces and lines connect various parts of the mobile device 120.

If the integrated module of the mobile device 120 is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer readable storage medium. Based on this understanding, the present invention implements the above-mentioned embodiment method All or part of the processes in the computer program can also be used to instruct the relevant hardware to complete the computer program. The computer program can be stored in a computer-readable storage medium. When the computer program is executed by the processor, the computer program can realize each of the above Steps of method embodiment. It should be noted that the content contained in the computer-readable medium can be appropriately added or deleted according to the requirements of the legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to the legislation and patent practice, the computer-readable medium Does not include electrical carrier signals and telecommunication signals.

The mobile device 120 provided in this embodiment applies artificial intelligence-enabled medical treatment to the mobile device, so that the mobile device can be matched with the ultrasound probe 70 to complete the trauma ultrasound diagnosis, which can quickly and accurately perform the ultrasound scan in the medical staff in real time. Determine whether there is blood and effusion in the detected part of the subject, as well as its specific location and size. Therefore, with the assistance of artificial intelligence, even medical staff without extensive experience in ultrasound use can quickly interpret trauma ultrasound video images and make corresponding treatment measures. Therefore, the trauma ultrasound diagnosis method provided in this embodiment reduces the requirements for related skills of medical staff and greatly improves the accuracy and efficiency of ultrasound diagnosis. The application of trauma ultrasound diagnosis to mobile devices can also avoid dependence on mobile networks.

Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit them. Although the present invention has been described in detail with reference to the preferred embodiments, those of ordinary skill in the art should understand that the technical solutions of the present invention can be Modifications or equivalent replacements are made without departing from the spirit and scope of the technical solution of the present invention.

Claims (24)

1. A trauma ultrasound diagnosis method, which is characterized in that it comprises:

   Acquiring an electrical signal converted by an ultrasonic echo and generating an ultrasonic image video stream, the ultrasonic echo being the ultrasonic echo received when the ultrasonic probe detects the subject;

   Display ultrasound video images;

   Use an artificial intelligence model to analyze the detected part based on the ultrasound image video stream to determine whether there is a liquid dark area; and

   The analysis result of the artificial intelligence model is displayed.
2. The trauma ultrasound diagnosis method of claim 1, further comprising: if there is a liquid dark area, segmenting the liquid dark area using the artificial intelligence model, and analyzing the orientation of the liquid dark area , Shape and/or size.
3. The wound ultrasound diagnosis method according to claim 2, wherein the analysis result is that there is no liquid dark area or the distribution, shape and/or size of the liquid dark area.
4. The trauma ultrasonic diagnosis method of claim 2, wherein if there is a liquid dark area, the analysis result of the artificial intelligence model is displayed as: outputting the name of the part where the liquid dark area exists, and superimposing the liquid dark area Segment the masked ultrasonic video image frame, and/or output the position, size and/or shape of each liquid dark area.
5. The trauma ultrasound diagnosis method according to claim 1 or 2, wherein the artificial intelligence model adopts a lightweight deep convolutional neural network as a model, and the trauma ultrasound diagnosis method is implemented by a mobile device.
6. The trauma ultrasound diagnosis method according to claim 1, wherein the "using an artificial intelligence model to analyze the detected part based on the ultrasound image video stream to determine whether there is a liquid dark area" comprises: using a fully convolutional nerve The network performs multiple convolution operations on each frame of ultrasound video image to obtain one or more feature images. The one or more feature images obtain a probability after passing through the global pooling layer, the fully connected layer, and the activation function. Probability to judge whether there is a liquid dark zone.
7. The trauma ultrasound diagnosis method according to claim 2, wherein the "use the artificial intelligence model to segment the liquid dark area, and analyze the orientation, shape and/or size of the liquid dark area" Including: using a fully convolutional neural network to perform multiple convolution operations on each frame of ultrasound video image to obtain one or more feature images, each of the one or more feature images is then 1*1 convolution A numerical matrix with the same size as the frame of the ultrasound video image is obtained by calculation, each value in the numerical matrix corresponds to a pixel of the frame of the ultrasound video image, and the numerical matrix is divided by an activation function to obtain a value corresponding to the liquid dark zone According to the numerical distribution, the area and/or the amount of liquid accumulation of each liquid dark zone are calculated.
8. The trauma ultrasound diagnosis method of claim 1 or 2, further comprising: forming an artificial intelligence-assisted diagnosis and treatment report, and displaying and/or storing the artificial intelligence-assisted diagnosis and treatment report.
9. 8. The trauma ultrasound diagnosis method of claim 8, further comprising: using an artificial intelligence model to analyze which part of the diagnosed part is.
10. The trauma ultrasound diagnosis method of claim 9, further comprising: using an artificial intelligence model to determine whether all the diagnostic parts that need to be diagnosed have been diagnosed, and if all the diagnostic parts that need to be diagnosed have been diagnosed, then displaying the The analysis result of the artificial intelligence model and/or the formation of the artificial intelligence-assisted diagnosis and treatment report. If any diagnosis part that needs to be diagnosed has not been diagnosed, then continue to obtain the electrical signal converted by the ultrasonic echo.
11. A trauma ultrasound diagnosis system, characterized in that the system includes:

    A signal acquisition module for acquiring an electrical signal converted by an ultrasonic echo, the ultrasonic echo being the ultrasonic echo received when the ultrasonic probe detects the subject;

    A video image signal generating module, configured to generate an ultrasound image video stream according to the electrical signal;

    Artificial intelligence model, the artificial intelligence model includes:

    The liquid dark area judgment module is configured to receive the ultrasound image video stream, and analyze whether there is a liquid dark area in the detection part based on the ultrasound image video stream; and

    The display module is used for receiving the ultrasound image video stream and displaying the ultrasound video image on a display unit, and for receiving the analysis result of the artificial intelligence model and displaying the analysis result on the display unit.
12. The system of claim 11, wherein the artificial intelligence model further comprises: a liquid dark area segmentation module, the liquid dark area segmentation module is used to segment the liquid dark area, and analyze the liquid dark area The orientation, shape and/or size of the dark area.
13. The system according to claim 12, wherein the analysis result of the artificial intelligence model is that there is no liquid dark zone or the distribution, shape and/or size of the liquid dark zone.
14. The system of claim 12, wherein the display module is used to output the name of the part where the liquid dark area exists, the ultrasonic video image frame superimposed with the liquid dark area segmentation mask, and/or output each block The orientation, size and/or shape of the liquid dark zone.
15. The system according to claim 11 or 12, wherein the artificial intelligence model uses a lightweight deep convolutional neural network as a model, and the system is installed on a mobile device.
16. The system according to claim 11, wherein the liquid dark area judgment module is configured to perform multiple convolution operations on each frame of ultrasound video image by using a fully convolutional neural network to obtain one or more characteristic images, After passing the one or more characteristic images through the global pooling layer, the fully connected layer, and the activation function, a probability is obtained, and whether there is a liquid dark zone is determined according to the probability.
17. The system of claim 12, wherein the liquid dark area segmentation module is used to perform multiple convolution operations on each frame of ultrasound video image by using a fully convolutional neural network to obtain one or more characteristic images, Each feature image of the one or more feature images is subjected to a 1*1 convolution operation to obtain a numerical matrix with the same size as the frame of ultrasound video image, wherein each value in the numerical matrix corresponds to the frame of ultrasound For a pixel point of the video image, the liquid dark area segmentation module is also used to segment the numerical value distribution of the corresponding liquid dark area from the numerical matrix, and calculate the value of each liquid dark area according to the numerical value distribution. Area and/or effusion volume.
18. The system according to claim 11 or 12, wherein the system further comprises an auxiliary diagnosis and treatment report generation module, the auxiliary diagnosis and treatment report generation module is configured to generate an artificial intelligence auxiliary diagnosis and treatment report according to the analysis result of the artificial intelligence model .
19. The system according to claim 18, wherein the system further comprises an auxiliary diagnosis and treatment report storage module, and the auxiliary diagnosis and treatment report storage module is configured to store the artificial intelligence auxiliary diagnosis and treatment report.
20. The system according to claim 18 or 19, wherein the display module is also used to display the artificial intelligence-assisted diagnosis and treatment report.
21. 21. The system according to claim 18, wherein the system further comprises a diagnosis part judgment module, and the diagnosis part judgment module is used to judge which part the diagnosed part is.
22. The system of claim 21, wherein the system further comprises a diagnosis completion judging module, the diagnosis completion judging module is used to judge whether all the diagnosed parts that need to be diagnosed have been diagnosed, and the auxiliary diagnosis and treatment report generation module is used When the diagnosis completion judgment module judges that all the diagnosis parts that need to be diagnosed have been diagnosed, the auxiliary diagnosis and treatment report is generated, and/or the display module is used for judging all the diagnosis needs to be diagnosed by the diagnosis completion judgment module When the part has been diagnosed, the analysis result of the artificial intelligence model is displayed on the display unit.
23. A mobile device, characterized in that it comprises:

    Communication unit

    Display unit;

    Processing unit

    A storage unit, wherein a plurality of program modules are stored in the storage unit, and the plurality of program modules are loaded by the processing unit and execute the wound ultrasound diagnosis method according to any one of claims 1-10.
24. A computer-readable storage medium having a computer program stored thereon, wherein the computer program is executed by a processing unit to implement the wound ultrasound diagnosis method according to any one of claims 1-10.

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