Medical behavior execution time identification and analysis system and method

Abstract

The invention provides a medical behavior execution time identification and analysis system and method. The medical behavior execution time identification and analysis method comprises the following steps: capturing a to-be-detected medical image; identifying the to-be-tested medical image to analyze medical behaviors in the to-be-tested medical image; and recording the execution time point of the identified medical behavior.

Description

Technical Field

[0001] The present invention relates to medical imaging, and in particular to a medical behavior execution time recognition and analysis system and method. Background Art

[0002] In the operating room, patients cannot communicate directly and autonomously during anesthesia and invasive treatment. They must rely on the teamwork of medical staff and the implementation of patient identification, surgery and site marking to avoid medical accidents. However, according to foreign statistics, half of the medical quality and safety incidents in hospitals are related to operating room activities, and some of them can be avoided. The members involved in the work of the operating room are complex, including surgeons, anesthesiologists, operating room and anesthesia nurses, etc. Good communication and seamless teamwork are required to complete such a high-risk task together. The traditional surgical recording method is slow, and the accuracy is significantly reduced when facing complex scenarios. It is often not completed on time after the patient's surgery. It is also easy to cause medical accidents due to sloppy handwriting during manual transcription. Because accidents are difficult to avoid 100%, if one link is not confirmed, it may cause quality and safety incidents during the operation. Summary of the invention

[0003] In view of the deficiencies of the prior art, the present invention provides a medical behavior execution time identification and analysis system. The medical behavior execution time identification and analysis system of the present invention comprises a test image capture module, a medical image recognition module and a medical behavior time analysis module. The test image capture module is configured to capture the test medical image. The medical image recognition module is configured to identify the test medical image received from the test image capture module to analyze the medical behavior in the test medical image. The medical behavior time analysis module is configured to record the execution time point of the medical behavior identified by the medical image recognition module.

[0004] In view of the shortcomings of the prior art, the present invention provides a method for identifying and analyzing the execution time of medical behavior. The method for identifying and analyzing the execution time of medical behavior of the present invention comprises the following steps: capturing the medical image to be tested; identifying the medical image to be tested to analyze the medical behavior in the medical image to be tested; and recording the execution time point of the identified medical behavior.

[0005] As described above, the present invention provides a system and method for identifying and analyzing the execution time of medical behavior. The system and method for identifying and analyzing the execution time of medical behavior of the present invention can automatically identify in real time the medical images captured in the medical space area (such as but not limited to the operating room) to analyze the medical behaviors performed by the medical staff in the medical images, and can play or display in real time to the medical staff to see whether their own medical behaviors are accurate, thereby improving the safety of patients. Furthermore, the system and method for identifying and analyzing the execution time of medical behavior of the present invention can record in real time the execution time point of each medical behavior performed by the identified and analyzed medical staff, and provide it to the medical staff in the medical space (such as the operating room) for reference at any time, so that the medical staff can control the progress at any time, reduce the time pressure of the medical staff, and thus enable the operation to be carried out more smoothly and on time, reduce the infection rate of patients, and improve the turnover rate of the operating room. The system and method for identifying and analyzing the execution time of medical behavior of the present invention replaces the traditional manual recording of medical processes (such as surgical processes), which can effectively avoid the mistakes and communication gaps that often occur in manual work. The medical behavior execution time recognition and analysis system and method of the present invention can also be used to recognize captured medical images of the medical space to analyze the image feature values ​​of patients entering the medical space to prevent misidentification of patients.

[0006] To further understand the features and technical contents of the present invention, please refer to the following detailed description and drawings of the present invention. However, the drawings provided are only for reference and description and are not intended to limit the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0007] Figure 1 This is a block diagram of the medical behavior execution time recognition and analysis system according to the first embodiment of the present invention.

[0008] Figure 2 This is a flow chart of the steps of the method for performing time recognition and analysis of medical behavior according to the first embodiment of the present invention.

[0009] Figure 3 This is a block diagram of a medical behavior execution time recognition and analysis system according to a second embodiment of the present invention.

[0010] Figure 4 This is a flow chart of the steps of the method for performing time recognition and analysis of medical behavior according to the second embodiment of the present invention.

[0011] Figure 5 This is a block diagram of a medical behavior execution time recognition and analysis system according to a third embodiment of the present invention.

[0012] Figure 6 This is a flowchart of the steps of the method for performing time recognition and analysis of medical behavior according to the third embodiment of the present invention.

[0013] Figure 7This is a flowchart of the steps of the method for performing time recognition and analysis of medical behavior according to the fourth embodiment of the present invention.

[0014] Figure 8 This is a flowchart of the steps of the method for performing time recognition and analysis of medical behavior according to the fifth embodiment of the present invention.

[0015] Description of Figure Numbers

[0016] 100: Image capture module to be tested

[0017] 200: Medical image recognition module

[0018] 300: Medical behavior time analysis module

[0019] 400: Training model building module

[0020] 500: Medical Image Preprocessing Module

[0021] S11~S14, S21~S23, S31, S32, S41~S410, S51~S55: Steps DETAILED DESCRIPTION

[0022] The following is an explanation of the embodiments of the present invention through specific embodiments. Those skilled in the art can understand the advantages and effects of the present invention from the contents disclosed in this specification. The present invention can be implemented or applied through other different specific embodiments, and the details in this specification can also be modified and changed in various ways based on different viewpoints and applications without departing from the concept of the present invention. In addition, it is stated in advance that the drawings of the present invention are only simple schematic illustrations and are not depicted according to actual dimensions. The following embodiments will further explain the relevant technical contents of the present invention in detail, but the disclosed contents are not intended to limit the scope of protection of the present invention. In addition, the term "or" used herein may include any one or more combinations of the associated listed items depending on the actual situation.

[0023] Reference Figure 1 and Figure 2 ,in Figure 1 is a block diagram of a medical behavior execution time recognition and analysis system according to a first embodiment of the present invention, Figure 2 This is a flow chart of the steps of the method for performing time recognition and analysis of medical behavior according to the first embodiment of the present invention.

[0024] like Figure 1As shown, the medical behavior execution time recognition and analysis system of the present invention comprises an image capture module 100 to be tested, a medical image recognition module 200 and a medical behavior time analysis module 300. The image capture module 100 to be tested can be a hardware device such as a camera, a video camera or other image capture device with an image capture function. The medical image recognition module 200 and the medical behavior time analysis module 300 can be a processor, a central controller, a cloud server or other hardware devices.

[0025] like Figure 1 The medical behavior execution time recognition and analysis system of the present invention is suitable for executing the medical behavior execution time recognition and analysis method of the present invention. Figure 2 Steps S11 to S14 are shown.

[0026] First, the image capture module 100 to be tested captures one or more images (such as Figure 2 For the convenience of explanation, the following description is based on a single medical image to be tested. In practice, the same or similar activities can be performed on multiple medical images to be tested.

[0027] The medical image recognition module 200 recognizes the medical image to be tested (eg, Figure 2 Step S12) to analyze the medical behavior of the medical personnel in the medical image to be tested (such as Figure 2 Step S13).

[0028] The medical behavior time analysis module 300 obtains the medical behavior in the medical image to be tested identified by the medical image recognition module 200 from the medical image recognition module 200 , and obtains the capture time point when the medical image to be tested is captured by the image capture module 100 from the image capture module 100 .

[0029] The medical behavior time analysis module 300 records the medical behavior in the medical image to be tested and the execution time of the medical behavior (such as the time point at which the medical image to be tested is captured by the image capture module 100) Figure 2 For example, the medical behavior time analysis module 300 records the capture time point of the medical image to be tested as the execution time point (this is the beginning time point) of the medical behavior in the medical image to be tested.

[0030] The medical behavior time analysis module 300 can record the captured medical images to be tested and the medical behaviors and their execution time points in the identified and analyzed medical images to be tested.

[0031] For example, the medical behaviors recorded by the medical behavior time analysis module 300 may include multiple behaviors of one or more medical personnel using medical instruments, medical equipment, medical consumables (such as but not limited to scalpels, syringes, medical testing instruments and other items in the medical space area) and medical agents (such as anesthetics or other various drugs, etc.) in a medical space area (such as but not limited to an operating room, a treatment room, and a recovery room).

[0032] The execution time points of medical behaviors recorded by the medical behavior time analysis module 300 may include multiple execution time points of multiple behaviors of one or more medical personnel using medical instruments, medical equipment, medical consumables and medical drugs in the medical space area.

[0033] Additionally or alternatively, the medical behaviors recorded by the medical behavior time analysis module 300 may include behaviors of one or more users (including medical personnel, patients, family members of patients, cleaning staff, etc.) entering and leaving the medical space area and moving between multiple locations in the medical space area. The execution time points of the medical behaviors recorded by the medical behavior time analysis module 300 may include multiple execution time points of behaviors of one or more medical personnel and patients entering and leaving the medical space area and multiple execution time points of moving between multiple locations in the medical space area.

[0034] More specifically, if the medical behavior execution time identification and analysis system and method of the present invention are applied to an operating room, the execution time points recorded by the medical behavior time analysis module 300 include the time point when the medical staff checks on the patient, the time point when the patient changes into surgical clothes and arrives at the operating room, the time point when the patient enters the operating room, the time point when the patient begins to be anesthetized, the time point when the patient begins to be completely anesthetized, the time point when the medical staff places the patient in the position and posture for surgery, the time point when the medical staff disinfects, the time point when the medical staff lays the sheet, the time point when the medical staff operates, the time point when the medical staff sutures the wound, the time point when the medical staff withdraws the sheet, the time point when the patient wakes up after anesthesia, the time point when the patient leaves the operating room, the time point when the patient arrives at the recovery room, the time point when the cleaning staff enters the medical space, the time point when the cleaning staff cleans the medical space, the time point when the cleaning staff leaves the medical space, or any combination thereof.

[0035] Reference Figure 3 and Figure 4 ,in Figure 3 is a block diagram of a medical behavior execution time recognition and analysis system according to a second embodiment of the present invention, Figure 4 This is a flow chart of the steps of the method for performing time recognition and analysis of medical behavior according to the second embodiment of the present invention.

[0036] The second embodiment of the present invention differs from the first embodiment in that Figure 3As shown, the medical behavior execution time recognition and analysis system of the second embodiment of the present invention includes not only a test image capturing module 100 , a medical image recognition module 200 and a medical behavior time analysis module 300 , but also a training model establishment module 400 .

[0037] like Figure 3 The medical behavior execution time recognition and analysis system of the present invention is suitable for executing the medical behavior execution time recognition and analysis method of the present invention. Figure 4 Steps S11 to S14 and S21 to S23 are shown.

[0038] In the model training mode, the training model building module 400 obtains sample medical images (such as sample medical images of patients and sample medical images of medical personnel) of medical behaviors (such as sample medical images of patients and sample medical images of medical personnel) from the database DB of the external electronic device or the cloud server. Figure 4 Step S21).

[0039] In the model training mode, the training model building module 400 trains the medical behavior machine learning model to identify the medical behavior in the sample medical image (such as Figure 4 Step S22).

[0040] In addition or alternatively, in the model training mode, the training model building module 400 trains the medical behavior machine learning model to identify the body parts of patients and medical personnel in the sample medical images based on multiple image feature values ​​of the body parts of patients and medical personnel (including face, hands, surgical sites, etc.) in one or more sample medical images.

[0041] Additionally or alternatively, in the model training mode, the training model building module 400 trains the medical behavior machine learning model to identify the roles and identities of patients and medical staff in the sample medical images based on multiple image feature values ​​of the patients' and medical staff's clothing (such as patient gowns, physician gowns, surgical gowns) in one or more sample medical images.

[0042] In the actual use mode, the image capture module 100 captures one or more images as the medical image to be tested (eg, Figure 4 Step S11).

[0043] In the actual use mode, the medical image recognition module 200 inputs the medical image to be tested captured by the image capture module 100 into the medical behavior machine learning model (such as the medical behavior machine learning model) trained by the training model building module 400. Figure 4 Step S23) to identify the medical behavior in the medical image to be tested (such as Figure 4Step S13) (and identifying the body parts and identities of the patient and medical staff).

[0044] In the actual use mode, the medical behavior time analysis module 300 records the medical behavior in the medical image to be tested and the execution time of the medical behavior (such as Figure 4 Step S14) includes the time point at which the medical personnel uses a scalpel to cut the wound of the patient's designated body part or performs other surgical procedures.

[0045] Reference Figure 5 and Figure 6 ,in Figure 5 is a block diagram of a medical behavior execution time recognition and analysis system according to a third embodiment of the present invention, Figure 6 This is a flowchart of the steps of the method for performing time recognition and analysis of medical behavior according to the third embodiment of the present invention.

[0046] The third embodiment of the present invention differs from the second embodiment in that: Figure 5 As shown, the medical behavior execution time recognition and analysis system of the third embodiment of the present invention not only includes a test image capture module 100, a medical image recognition module 200, a medical behavior time analysis module 300 and a training model establishment module 400, but also includes a medical image preprocessing module 500.

[0047] like Figure 5 The medical behavior execution time recognition and analysis system of the third embodiment of the present invention is suitable for executing the medical behavior execution time recognition and analysis method of the third embodiment of the present invention. Figure 6 Steps S21, S22, S11, S31, S23, S32, S13, S14 shown in Figure 2 The step S12 shown may include the following: Figure 6 Step S32 is shown. Steps S11, S13, S14, S21, and S22 are as described above and will not be described in detail below.

[0048] In the actual use mode, the medical image preprocessing module 500 performs a preprocessing procedure on the medical image to be tested containing the medical behavior captured by the image capture module 100, and then the medical image recognition module 200 recognizes the medical image to be tested after the preprocessing procedure is performed by the medical image preprocessing module 500.

[0049] For example, the medical image preprocessing module 500 includes a medical image segmentation unit. In the actual use mode, the medical image segmentation unit segments a medical image to be tested captured by the image capture module 100 (this is a preprocessing procedure) into a plurality of segmented medical images to be tested (such as Figure 6 Step S31).

[0050] In the actual use mode, the medical image recognition module 200 inputs each of the multiple segmented medical images to be tested cut out from a medical image to be tested received by the medical image segmentation unit into the medical behavior machine learning model (such as Figure 6 Step S23), in which a plurality of image feature values ​​of each of a plurality of segmented medical images to be tested are identified by a medical behavior machine learning model, and the medical behavior (such as Figure 6 Step S13).

[0051] Reference Figure 7 , which is a step flow chart of the medical behavior execution time identification and analysis method of the fourth embodiment of the present invention.

[0052] The medical behavior execution time identification and analysis method of the present invention may further include the following steps: Figure 7 Steps S41 to S410 shown in FIG. 4 can be performed as follows: Figure 3 or Figure 5 The medical behavior execution time identification and analysis system of the present invention is shown to be executed. It should be understood that the execution order of the steps in this article can be adjusted according to actual needs, and the execution order of some steps in this article can also be omitted.

[0053] In the model training mode, the training model building module 400 obtains a plurality of sample medical segmented behavior images (such as Figure 7 Step S41), wherein the one medical behavior includes the multiple medical segmented behaviors.

[0054] In the model training mode, the training model building module 400 trains the medical behavior machine learning model, and identifies multiple medical segmented behaviors (such as Figure 7 Step S42), and train the medical behavior machine learning model, based on multiple medical segmented behaviors to analyze a medical behavior composed of multiple coherent medical segmented behaviors in multiple sample medical segmented behavior images.

[0055] In the actual use mode, the image capture module 100 captures a plurality of medical segment images to be tested (eg Figure 7Step S43). For example, the image capture module 100 to be tested is a camera or other capture device with the function of capturing static images / photos, configured to capture multiple static images / photos in the same time interval as multiple medical segmented images to be tested. Alternatively, the image capture module 100 to be tested may be a camera or other capture device with the function of capturing dynamic images, configured to capture dynamic videos as medical images to be tested, and the multiple image segments at multiple time points contained in the medical video to be tested are used as multiple medical segmented images to be tested.

[0056] In the actual use mode, the medical image recognition module 200 inputs the multiple medical segmented images to be tested captured by the image capture module 100 into the medical behavior machine learning model (such as Figure 7 Step S44), to identify multiple image feature values ​​in multiple medical segmented images to be tested through the medical behavior machine learning model, so as to identify multiple medical segmented behaviors (such as Figure 7 Step S45).

[0057] In practice, if the processing efficiency is to be increased, the medical behavior execution time recognition and analysis system of the present invention may also include an image classification processing module, and the medical image recognition module 200 may include multiple medical image recognition units. The image classification processing module may classify multiple medical images to be tested captured by the image capture module to be tested, and output multiple images classified into the same group among the multiple medical images to be tested to the same medical image recognition unit for recognition. For example, the image classification processing module may classify multiple medical images to be tested captured in the same medical space area (e.g., the same operating room) into the same group.

[0058] In the actual use mode, the medical image recognition module 200 uses the training model building module 400 to analyze a medical behavior (such as a medical behavior) composed of a plurality of coherent medical segmented behaviors in a plurality of medical segmented behavior images to be tested based on the identified plurality of medical segmented behaviors in the plurality of medical segmented behavior images to be tested. Figure 7 Step S47).

[0059] In the actual use mode, the medical behavior time analysis module 300 uses the multiple capture time points of the multiple medical segmented images to be tested captured by the image capture module 100 as the multiple execution time points of the multiple medical segmented behaviors in the multiple medical segmented behavior images to be tested.

[0060] In the actual use mode, the medical behavior time analysis module 300 compares the multiple execution time points of the multiple medical segmented behaviors in the multiple medical segmented behavior images to be tested, so as to use the execution time point of the earliest executed medical segmented behavior among the multiple medical segmented behaviors as the start time point of the medical behavior, and use the execution time point of the latest executed medical segmented behavior among the multiple medical segmented behaviors as the completion time point of the medical behavior (such as Figure 7 Step S48).

[0061] In actual use mode, the medical behavior time analysis module 300 can calculate the time difference between the start time point and the completion time point of the medical behavior to obtain the execution time length of the medical behavior (such as Figure 7 Step S49).

[0062] The medical behavior time analysis module 300 records multiple medical segmented behavior images to be tested, multiple medical segmented behaviors identified and analyzed and their respective multiple execution time points, and records the identified medical behaviors and their start time points, completion time points and execution time lengths, and can be transmitted in real time to a display device in a medical space area (such as an operating room) or other space for playback or display. For example, the execution time point is marked as a timestamp on or next to the medical image to be tested so that medical personnel can watch and confirm the accuracy of their own medical behavior in real time. It can also be recorded or marked on electronic forms (including electronic login forms, electronic verification forms, electronic record forms) or other electronic files, and can also be played or displayed on a medical live broadcast platform or other network platform.

[0063] Reference Figure 8 , which is a step flow chart of the medical behavior execution time identification and analysis method of the fifth embodiment of the present invention.

[0064] The medical behavior execution time identification and analysis method of the present invention may also include the following steps: Figure 8 Steps S51 to S55 shown in FIG. 1 can be performed as follows: Figure 3 or Figure 5 The medical behavior execution time recognition and analysis system of the present invention is shown to be executed.

[0065] In the model training mode, the training model building module 400 is based on multiple execution time lengths of multiple medical segmented behaviors in multiple sample medical segmented behavior images and the execution time length of a medical behavior composed of multiple medical segmented behaviors (i.e., the sum of multiple execution time lengths of multiple medical segmented behaviors). The medical behavior machine learning model is trained to predict the subsequent other or last completion time point of multiple medical segmented behaviors included in a medical behavior, i.e., the completion time point of the last medical segmented behavior (e.g., the completion time point of the last medical segmented behavior) according to one or more execution time points of one or more sample medical segmented behavior images executed earlier. Figure 7 Step S51).

[0066] In the actual use mode, before the medical personnel have completed the medical behavior, the medical behavior time analysis module 300 samples one or more medical segmented behaviors from the identified multiple medical segmented behavior images to be tested (such as Figure 7 Step S52), using a medical behavior machine learning model (such as Figure 7 Step S53) predicts the completion time point of the medical behavior performed by the medical personnel (i.e., the last completion time point of the multiple medical segmented behaviors included in the medical behavior) based on the multiple execution time points of the sampled one or more currently executed medical segmented behaviors as the predicted completion time point (e.g., Figure 7 Step S54).

[0067] In the model training mode, the medical behavior time analysis module 300 calculates the error value between the predicted completion time point of the predicted medical behavior and the actual completion time point of the medical behavior actually completed by the medical staff, and inputs it into the training model establishment module 400. The training model establishment module 400 can retrain the medical behavior machine learning model based on this error value to make its prediction more accurate.

[0068] If necessary, the medical behavior time analysis module 300 can also calculate the error values ​​between the predicted completion time point and the actual completion time and the reference / standard completion time point. The medical behavior time analysis module 300 can transmit the predicted completion time point, the actual completion time and the above error values ​​to a display device in a medical space area (such as an operating room) or other space for playback or display, for example, marking the execution time point as a timestamp on or next to the medical image to be tested for reference by medical personnel.

[0069] In summary, the present invention provides a medical behavior execution time recognition and analysis system and method. The medical behavior execution time recognition and analysis system and method of the present invention can automatically and in real time recognize medical images captured in a medical space area (such as but not limited to an operating room) to analyze the medical behaviors performed by medical personnel in the medical images, and can be played or displayed in real time to medical personnel to see whether their own medical behaviors are accurate, thereby improving patient safety. Moreover, the medical behavior execution time recognition and analysis system and method of the present invention can record the execution time point of each medical behavior performed by the identified and analyzed medical personnel in real time, and provide it to the medical personnel in the medical space (such as an operating room) for reference at any time, so that the medical personnel can control the progress at any time, reduce the time pressure of the medical personnel, and thus enable the operation to be carried out more smoothly and on time, reduce the infection rate of the patient, and improve the turnover rate of the operating room. The medical behavior execution time recognition and analysis system and method of the present invention replace the traditional manual recording of the medical process (such as the surgical process), which can effectively avoid the mistakes and communication gaps that often occur in manual work. The medical behavior execution time recognition and analysis system and method of the present invention can also be used to recognize the captured medical images of the medical space to analyze the image feature values ​​of the patients entering the medical space to prevent the wrong patients.

[0070] The contents disclosed above are only preferred feasible embodiments of the present invention, and are not intended to limit the scope of the present invention. Therefore, all equivalent technical changes made using the contents of the present invention's specification and drawings are included in the scope of the present invention.

Claims

1. A medical behavior execution time recognition and analysis system, characterized in that: Include: An image capture module to be tested, configured to capture the medical image to be tested; A medical image recognition module, configured to recognize the medical image to be tested received from the image capture module to be tested, so as to analyze the medical behavior in the medical image to be tested; as well as The medical behavior time analysis module is configured to record the execution time point of the medical behavior identified by the medical image recognition module.

2. The medical behavior execution time recognition and analysis system according to claim 1, characterized in that: The execution time points of the medical behavior recorded by the medical behavior time analysis module include multiple time points when one or more medical personnel use medical instruments, medical equipment, medical consumables and medical drugs in the medical space area, one or more time points when the medical personnel move in the medical space, the time points when the patients move in the medical space or any combination thereof.

3. The medical behavior execution time recognition and analysis system according to claim 1, characterized in that: It also includes a medical image preprocessing module, configured to perform a preprocessing procedure on the medical image to be tested captured by the image capture module to be tested, The medical image recognition module is configured to recognize the medical image to be tested after executing the preprocessing procedure.

4. The medical behavior execution time recognition and analysis system according to claim 3, characterized in that: The medical image preprocessing module includes a medical image segmentation unit configured to segment the medical image to be tested captured by the image capture module to be tested into a plurality of segmented medical images to be tested, The medical image recognition module is configured to recognize each of the segmented medical images to be tested received from the medical image segmentation unit, so as to analyze the medical behavior in the medical images to be tested.

5. The medical behavior execution time recognition and analysis system according to claim 1, characterized in that: It also includes a training model building module, configured to train a medical behavior machine learning model, based on multiple image feature values ​​in the sample medical image, identify the medical behavior in the sample medical image, The medical image recognition module is configured to input the medical image to be tested and its multiple image feature values ​​into the medical behavior machine learning model trained by the training model establishment module, so as to identify the medical behavior in the medical image to be tested through the medical behavior machine learning model.

6. The medical behavior execution time recognition and analysis system according to claim 5, characterized in that: The training model building module is configured to train the medical behavior machine learning model to identify the multiple medical segmented behaviors included in the medical behavior based on the multiple sample medical segmented behavior images included in the sample medical image, The medical image recognition module is configured to use the medical behavior machine learning model to recognize a plurality of medical segmented behaviors in a plurality of medical segmented behavior images to be tested contained in the medical image to be tested, The medical behavior time analysis module is configured to record a plurality of execution time points of the plurality of medical segmented behaviors and the execution time length of the medical image to be tested.

7. The medical behavior execution time recognition and analysis system according to claim 6, characterized in that: The medical behavior time analysis module is configured to predict the completion time point of the medical behavior based on one or more of the multiple execution time points of each of the multiple medical segmented behaviors to be tested.

8. A method for identifying and analyzing the execution time of medical behavior, characterized in that: The following steps are involved: Capturing medical images to be tested; Identifying the medical image to be tested to analyze the medical behavior in the medical image to be tested; and The identified execution time point of the medical behavior is recorded.

9. The method for identifying and analyzing the execution time of medical behavior according to claim 8, characterized in that: It also includes the following steps: The execution time point includes recording multiple time points at which one or more medical personnel use medical instruments, medical equipment, medical consumables and medical drugs in the medical space area, the time points at which one or more medical personnel move in the medical space, the time points at which patients move in the medical space, or any combination thereof.

10. The method for identifying and analyzing the execution time of medical behavior according to claim 8, characterized in that: The method also includes the following steps before performing the identification of the medical image to be tested: A preprocessing procedure is performed on the captured medical image to be tested.

11. The method for identifying and analyzing the execution time of medical behavior according to claim 8, characterized in that: The method also includes the following steps before performing the identification of the medical image to be tested: Segmenting the captured medical image to be tested into a plurality of segmented medical images to be tested; and Each of the segmented medical images to be tested is identified to analyze the medical behavior in the medical images to be tested.

12. The method for identifying and analyzing the execution time of medical behavior according to claim 8, characterized in that: It also includes the following steps: Training a medical behavior machine learning model to identify the medical behavior in the sample medical image based on multiple image feature values ​​in the sample medical image; as well as The captured medical image to be tested and its multiple image feature values ​​are input into the trained medical behavior machine learning model to identify the medical behavior in the medical image to be tested through the medical behavior machine learning model.

13. The method for identifying and analyzing the execution time of medical behavior according to claim 8, characterized in that: It also includes the following steps: Based on a plurality of sample medical segmented behavior images included in the sample medical image, training the medical behavior machine learning model to identify a plurality of medical segmented behaviors included in the medical behavior; Using the medical behavior machine learning model, identifying a plurality of the medical segmented behaviors in a plurality of medical segmented behavior images to be tested contained in the medical image to be tested; Recording a plurality of execution time points of each of the plurality of medical segmentation behaviors; and The execution time length of executing the medical image to be tested is calculated according to the multiple execution time points of each of the multiple medical segmented behaviors.

14. The method for identifying and analyzing the execution time of medical behavior according to claim 13, characterized in that: It also includes the following steps: The completion time point of the medical behavior is predicted according to one or more of the plurality of execution time points of each of the plurality of medical segmented behaviors to be tested.

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