An endoscope operation training process standardization checking method and system

Abstract

The application discloses a kind of endoscope operation training process standardization checking method and system, belong to the technical field of standardization training and intelligent examination quality control of digestive endoscopist.The method establishes endoscope SOP rule base, checks the access qualification of student, and divides into five kinds of nodes such as preoperative preparation, mirror, intraoperative observation and diagnosis and treatment, mirror, postoperative review;Synchronous acquisition handle sensor, video under mirror, tutor voice and text comment data are fused and compared according to node and time sequence, and the results of defect level, error correction prompt and graduation qualification review are output, and encrypted traceability archives are formed.The application can improve the standardization, objectivity and traceability level of endoscope training examination.

Description

Technical Field

[0001] This invention relates to the field of standardized training and intelligent assessment quality control for digestive endoscopy physicians, specifically to a standardized verification method and system for endoscopic operation training processes based on a process node rule base, multi-source training data fusion verification, and full-cycle record storage. Background Technology

[0002] Digestive endoscopy is an important diagnostic and treatment technique in gastroenterology, and standardized training for endoscopists is a crucial component of standardized residency training in clinical medicine. Current endoscopic training typically employs a "mentor demonstration, trainee practice, and human evaluation" approach, where the supervising mentor observes the trainee's procedure based on their personal experience and then evaluates the trainee accordingly.

[0003] However, the above-mentioned traditional methods have the following shortcomings:

[0004] First, training assessments rely heavily on the personal experience of instructors. Different instructors may have different evaluation criteria for the same operating procedure, the same operating action, or the same mistake, resulting in a high degree of subjectivity in scoring and making it difficult to form a unified, quantifiable, and practical standardized verification standard.

[0005] Second, existing endoscopic simulation training equipment can usually only score simple movements or assess the difficulty of basic operations.

[0006] It is difficult to perform segmented compliance verification of the complete endoscopic procedure, and it is also difficult to identify details such as errors in the timing of the procedure, non-standard operation, missed detection of the field of view, and high-risk misoperation.

[0007] Third, the existing training system lacks a full-process traceability mechanism. The training process, error records, rectification status, and instructor evaluations of trainees are usually not systematically archived. The supervision of residency training, qualification review, and teaching quality monitoring lack objective data support, making it difficult to review and trace the quality of training.

[0008] Fourth, the existing training lacks a tiered graduation assessment mechanism, making it impossible to accurately distinguish between trainees' basic operational skills and minimally invasive treatment skills, and also making it difficult to automatically match specialized remedial training courses to the weaknesses of different trainees, thus affecting training efficiency and quality.

[0009] Therefore, there is an urgent need for a standardized verification method and system for the endoscopy operation training process that can achieve full-process node verification, intelligent judgment of multi-source data, full-process evidence storage and traceability, and hierarchical graduation verification. Summary of the Invention

[0010] Technical problems to be solved

[0011] The technical problem to be solved by this invention is to provide a standardized verification method and system for endoscopy operation training processes, addressing the issues of inconsistent standards for endoscopy training and assessment, subjective manual scoring, lack of process verification, inaccurate defect localization, untraceable training data, and lack of quantitative basis for graduation assessment.

[0012] Technical solution

[0013] To solve the above-mentioned technical problems, the present invention provides the following technical solution.

[0014] This invention provides a method for standardizing and verifying endoscopic operation training procedures, comprising the following steps:

[0015] S1. Rule Base Configuration and Training Access Qualification Verification: Establish a SOP (Standard Operating Procedure) rule base corresponding to endoscopic procedures and training stages. The SOP rule base stores process nodes, node execution order, node compliance thresholds, prohibited operation list, and high-risk operation judgment criteria. The system retrieves the trainee's endoscopic theoretical examination scores, training hours, and pre-requisite course completion data, and compares them with preset training access conditions. When the theoretical examination score is not up to standard or the training hours are insufficient, the trainee's practical training access is locked and the theoretical supplementary training channel is opened.

[0016] S2. Standardized Verification of Segmented Nodes in the Entire Process: After granting access to practical training, the endoscopic practical training process is broken down into five fixed process nodes: preoperative preparation, standardized operation of the endoscope, intraoperative cavity observation and diagnosis, observation throughout the withdrawal process, and postoperative review and conclusion. Corresponding process node identifiers and time sequence information are configured for the data collected during the practical training.

[0017] S3. Synchronous acquisition of multi-source data: When trainees are conducting hands-on training using the simulated endoscope handle and the digestive tract simulation model, multi-dimensional sensor data of the simulated endoscope handle, real-time video image data under the endoscope, and voice and text comments from the instructor are collected simultaneously.

[0018] S4. Multi-source data fusion and intelligent verification of action compliance: Based on the process node identifier and time sequence information, the multi-dimensional sensor data of the simulated endoscope handle, real-time video image data under the endoscope, and the voice and text commentary data of the instructor in the same process node are associated, and operation mechanics compliance verification, operation field of view and action sequence AI recognition verification, and teaching defect structured annotation verification are completed respectively. The above verification results are compared with the SOP rule base to comprehensively determine the operation compliance level and mark minor defects, moderate violations and fatal high-risk operation errors.

[0019] S5. Encrypted and traceable full-cycle training data: The verification results, defect locations, operation time, error correction records and instructor evaluation data of each process node are encrypted and archived to establish a traceable file for each person's full training cycle.

[0020] S6. Graduation Qualification Review and Verification: The system automatically summarizes the student's full-cycle segmented verification scores, error frequency, high-risk operation clearing status, and supplementary training completion records. Based on the preset grading threshold, it automatically determines the student's graduation qualification level; for students who do not meet the standards, it automatically matches targeted special supplementary training courses to complete closed-loop rectification training.

[0021] Furthermore, the verification items for the preoperative preparation node include verification of instrument count completeness, preoperative patient assessment process, standardization of patient positioning, and compliance of equipment debugging parameters; the verification items for the standardized operation node of endoscope insertion include verification of insertion depth, endoscope torsion angle, inflation volume control, insertion sequence, and compliance of anti-roughness wall operation; the verification items for the intraoperative cavity observation and treatment operation node include verification of mucosal observation coverage, blind spot ratio, biopsy operation sequence, polyp removal operation sequence, and standardization of hemostasis; the verification items for the endoscope withdrawal observation node include verification of withdrawal speed uniformity, total observation time, completeness of ileocecal re-examination, and completeness of key area re-examination; the verification items for the postoperative debriefing and conclusion node include verification of training record completeness, completion of defect debriefing, submission of mentor evaluation, and generation of supplementary training suggestions.

[0022] Furthermore, the multi-dimensional sensing data of the simulated endoscope handle includes endoscope insertion depth data, endoscope torsion angle data, inflation pressure data, and operating force data; the operation mechanics compliance verification includes comparing the above data with the node compliance threshold of the corresponding process node to determine whether there are abnormal insertion depth, abnormal endoscope torsion angle, abnormal inflation volume control, abnormal operating force, or abnormal advance and retreat movements.

[0023] Furthermore, the AI ​​recognition and verification of the operational field of view and action sequence includes: performing endoscopic field of view target detection, mucosal missed diagnosis area identification, excessive air injection identification, endoscope body violent push identification, field of view omission identification, and operation sequence inversion identification on real-time video image data under endoscopy, and matching the identified operation actions with the node execution order in the SOP rule base in combination with the time sequence information; when missing actions, inverted action sequence, insufficient observation coverage, excessively rapid endoscope withdrawal, or high-risk operations are identified, real-time error correction prompts are generated and corresponding defect markers are formed.

[0024] Furthermore, the structured annotation and verification of teaching defects includes: converting the instructor's voice into text data, performing structured processing on the text data and the instructor's text comment data, extracting defect categories, defect severity and correction suggestions, and associating the defect categories, defect severity and correction suggestions with the operation mechanics compliance verification results, operation field of view and action timing AI recognition verification results of the corresponding process nodes.

[0025] Furthermore, the minor defects are used to trigger real-time error correction prompts; the moderate violations are used to be included in the segmented verification score and defect list of the corresponding process node; and the fatal high-risk operational errors are used to trigger high-risk warnings and serve as a limiting condition in the graduation tiered qualification review and verification.

[0026] Furthermore, the graduation qualification levels include the internship operation qualification level, the basic independent operation qualification level, and the minimally invasive treatment operation qualification level. When a trainee does not reach the corresponding graduation qualification level, the system will automatically match specialized retraining courses related to the standard operation of endoscope insertion, cavity observation, endoscope withdrawal observation, biopsy operation, polyp removal operation, or hemostasis operation according to the type of deficiency.

[0027] The present invention also provides a standardization verification system for endoscopy operation training procedures, which is used to implement the above-mentioned standardization verification method for endoscopy operation training procedures. The system includes a practical data acquisition hardware terminal, a cloud-based intelligent standardization verification platform, and a local teaching management client.

[0028] The practical data acquisition hardware terminal includes a simulated endoscope operating handle with multi-dimensional torque and displacement sensors, a digestive tract simulation model, a high-definition endoscope video acquisition module, an audio acquisition module, and an Internet of Things data transmission module, which are used to collect students' practical mechanical data, endoscopic image data, and instructor voice data in real time.

[0029] The cloud-based intelligent standardized verification platform includes an SOP rule base module, an AI intelligent verification engine module, a hierarchical permission management module, an encrypted file storage module, and an intelligent supplementary training push module. The local teaching management client is deployed on the teaching terminal and is used to realize training course arrangement, dynamic monitoring of practical processes, real-time violation pop-up reminders, automatic generation of verification reports, and export of graduation assessment reports.

[0030] Furthermore, the SOP rule base module incorporates standardized operation thresholds, compliance duration parameters, prohibited operation lists, and high-risk operation judgment criteria for various endoscopic procedures, including gastroscopy, colonoscopy, ERCP (endoscopic retrograde cholangiopancreatography), ESD (endoscopic submucosal dissection), and EUS (endoscopic ultrasound). It also supports iterative updates of the parameter base according to residency training guidelines.

[0031] Furthermore, the AI ​​intelligent verification engine module includes a visual detection unit, a temporal matching unit, a mechanical parameter comparison unit, and a speech NLP (Natural Language Processing) structured parsing unit, used to realize the fusion analysis of multi-source data and automated compliance judgment; the hierarchical permission management module sets four levels of permissions: student end, instructor end, training base administrator end, and residency training supervision end, used to realize practical learning, real-time instructor review, batch student management, and overall training quality supervision and statistics, respectively.

[0032] Beneficial effects

[0033] Compared with existing technologies, the present invention provides a method and system for standardizing and verifying endoscopic operation training procedures, which has the following beneficial effects:

[0034] 1. This invention verifies trainees' theoretical assessment scores, training hours, and completion of prerequisite courses through training access qualification verification, thereby preventing unqualified trainees from directly entering the practical training stage.

[0035] 2. This invention breaks down the complete endoscopic practical training process into five fixed process nodes: preoperative preparation, standardized operation of endoscope insertion, intraoperative cavity observation and diagnosis, observation throughout the endoscope withdrawal process, and postoperative review and conclusion. The invention also configures process node identifiers and time sequence information for the collected data to achieve standardized verification of the entire process in segments.

[0036] 3. This invention uses a SOP rule base to pre-set process nodes, node execution order, node compliance thresholds, prohibited operation lists, and high-risk operation judgment standards, so that the compliance judgment of each process node has a unified basis and reduces the subjective differences of traditional manual scoring.

[0037] 4. This invention integrates multi-dimensional sensor data from a simulated endoscope handle, real-time video image data under endoscopy, and voice and text feedback data from instructors, enabling a comprehensive assessment of the compliance level of operations from multiple dimensions, including mechanical parameters, field of view recognition, action timing, and teaching feedback.

[0038] 5. This invention can classify and mark minor defects, moderate violations and fatal high-risk operational errors, and realize dynamic real-time error correction prompts through a local teaching management client, thereby improving the efficiency of defect location and error correction.

[0039] 6. This invention establishes a traceability archive for each individual throughout the entire training cycle by encrypting and retrospectively storing training data throughout the entire training cycle. This makes the training process traceable, verifiable, and tamper-proof, providing objective data support for residency training supervision and training quality monitoring.

[0040] 7. This invention classifies trainees' graduation qualifications into three levels—internship operation, basic independent operation, and minimally invasive treatment operation—through a tiered qualification review and verification process. It also automatically matches trainees who do not meet the standards with specialized remedial training courses, thereby improving the effectiveness of the closed-loop training process.

[0041] 8. This invention is applicable to training and assessment of various endoscopic procedures such as gastroscopy, colonoscopy, ERCP, ESD, and EUS, and has strong promotion and application value. Attached Figure Description

[0042] Figure 1 This is a block diagram of the overall system architecture of the present invention;

[0043] Figure 2 This is a closed-loop flowchart of the standardization and verification method for endoscopic operation training procedures of the present invention.

[0044] Figure 3 This is a schematic diagram of the hardware terminal structure for data acquisition in practical operation according to the present invention;

[0045] Figure 4 This is a schematic diagram of the data fusion processing flow of the intelligent verification engine of the present invention. Detailed Implementation

[0046] The technical solution of the present invention will be further described below with reference to the accompanying drawings. It should be understood that the following embodiments are used to explain the present invention, and are not intended to limit the scope of protection of the present invention.

[0047] Unless otherwise specified in this instruction manual, the content regarding trainee training, patient assessment, endoscopic observation, and diagnostic and treatment procedures refers to simulated cases, simulated cavities, training videos, and simulated operations in medical simulation teaching or standardized training and assessment scenarios, and does not constitute a method for directly diagnosing or treating diseases on real patients.

[0048] Example 1: Standardized Verification of the Entire Colonoscopy Training Process

[0049] Reference Appendix Figure 1 To be continued Figure 4 During the standardized colonoscopy training process, once trainees complete the endoscopy theory exam and meet the admission requirements, the system automatically passes the admission qualification verification and grants them access to colonoscopy practical training.

[0050] After training begins, the hands-on data acquisition hardware terminal collects real-time data on endoscope insertion depth, endoscope torsion angle, inflation pressure, and operational force; the high-definition endoscope video acquisition module acquires real-time endoscopic images; and the audio acquisition module collects instructor-led audio data. The system configures corresponding process node identifiers and time sequence information for the above data so that different types of data can be associated with the same training process node in subsequent verification.

[0051] The system performs segmented node verification according to the training process, including preoperative preparation, intestinal cavity insertion, continuous observation, key re-examination, and uniform withdrawal. The AI ​​intelligent verification engine identifies violations in real time, such as insertion technique, intestinal cavity field coverage, forceful wall pushing, excessive insufflation, excessively rapid withdrawal, and omission of blind spots.

[0052] When the system detects non-standard operations, the local teaching management client issues a real-time pop-up warning and records the defect level. After the training, the cloud-based intelligent standardization verification platform summarizes all verification data and generates an individual colonoscopy operation standardization score report and defect list. For trainees with missed detections or rough operation issues, the system automatically pushes a special retraining course on intestinal observation. Only after completing the retraining and meeting the standards can they proceed to the next stage of training.

[0053] Example 2: Standardization and Verification of Introductory Gastroscopy Operation

[0054] Reference Appendix Figure 2 and attached Figure 4 For novice trainees learning basic gastroscopy procedures, the system is configured with a Standard Operating Procedure (SOP) rule base corresponding to introductory gastroscopy operations. This rule base focuses on setting verification items for patient positioning, equipment adjustment, gentle insertion of the endoscope, proper pharyngeal passage, panoramic observation of the gastric fundus, body, and antrum, and the debriefing procedure for endoscope withdrawal.

[0055] During training, a simulated endoscope handle collects data on the trainee's operating force, endoscope angle, and advance / retreat movements; a high-definition endoscope video acquisition module captures images of the gastroscopy field of view. An AI-powered intelligent verification engine uses the collected data to determine if there are issues such as excessive force, deviations in the endoscope angle, or disjointed observations of the field of view.

[0056] When improper endoscope insertion techniques or incomplete observation procedures are detected, the local teaching management client outputs real-time error correction prompts. After training, the system generates a standardized verification report for introductory gastroscopy operations and pushes basic operation retraining courses based on the trainees' deficiencies to help novice trainees establish standardized operating habits.

[0057] Example 3: Special Verification of Endoscopic Minimally Invasive Treatment Procedures

[0058] Reference Appendix Figure 1 and attached Figure 4 For training on endoscopic minimally invasive procedures such as ESD and polyp removal, the system has enabled a special verification mode for high-risk procedures.

[0059] In this mode, the system focuses on verifying the accuracy of biopsy positioning, the standardization of mucosal lifting and injection, the timing of dissection procedures, the compliance of hemostasis procedures, and the avoidance of high-risk procedures. The AI ​​intelligent verification engine combines microscopic video image data, operational mechanics data, and instructor-led audio and text data to identify and mark high-risk errors such as reversed operation sequence, blind dissection, and excessive electrocoagulation.

[0060] For operations marked as high-risk errors, the system includes them in the graduation qualification review criteria. If a trainee has uncorrected high-risk operational errors, the system will not determine that they have reached the qualification level for minimally invasive treatment operations and will automatically recommend corresponding specialized retraining courses. This method ensures the safety and standardization of endoscopic minimally invasive treatment operation training.

[0061] The standardized verification method and system for endoscopic operation training procedures provided by this invention can be deployed in hospital endoscopy centers, medical simulation teaching centers, standardized residency training bases, and regional training quality control platforms. This system is compatible with training and assessment for various endoscopic procedures such as gastroscopy, colonoscopy, ERCP, ESD, and EUS. It enables automatic data collection, intelligent error correction, defect localization, tiered graduation assessment, and full-process record archiving, demonstrating significant practicality and potential for widespread application.

[0062] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A method for standardizing and verifying endoscopic operation training procedures, characterized in that, Includes the following steps: S1. Rule Base Configuration and Training Access Qualification Verification: Establish a SOP (Standard Operating Procedure) rule base corresponding to endoscopic procedures and training stages. The SOP rule base stores process nodes, node execution order, node compliance thresholds, prohibited operation lists, and high-risk operation judgment standards. The system retrieves the trainee's endoscopic theoretical examination scores, training hours, and pre-requisite course completion data, and compares them with preset training access conditions. When the theoretical examination score is not up to standard or the training hours are insufficient, the trainee's practical training access is locked, and the theoretical supplementary training channel is opened. S2. Standardized verification of segmented nodes throughout the entire process: After granting access to practical training, the endoscopic practical training process is broken down into five fixed process nodes: preoperative preparation, standardized operation of endoscope insertion, intraoperative cavity observation and diagnosis, observation throughout the withdrawal of endoscope, and postoperative review and conclusion. Corresponding process node identifiers and time sequence information are configured for the data collected during the practical training. S3. Synchronous acquisition of multi-source data: When trainees are using the simulated endoscope operating handle and the digestive tract simulation model for practical training, multi-dimensional sensor data of the simulated endoscope handle, real-time video image data under the endoscope, and voice and text comments of the instructor are collected simultaneously. S4. Multi-source data fusion and intelligent verification of action compliance: Based on the process node identifier and time sequence information, the multi-dimensional sensor data of the simulated endoscope handle, real-time video image data under the endoscope, and the voice and text comment data of the instructor in the same process node are associated, and operation mechanics compliance verification, operation field of view and action sequence AI recognition verification, and teaching defect structured annotation verification are completed respectively; the above verification results are compared with the SOP rule base to comprehensively determine the operation compliance level and mark minor defects, moderate violations and fatal high-risk operation errors; S5. Encrypted and traceable full-cycle training data: The verification results, defect locations, operation time, error correction records and instructor evaluation data of each process node are encrypted and archived to establish a traceable file for each person's full training cycle. S6. Graduation Qualification Review and Verification: The system automatically summarizes the student's full-cycle segmented verification scores, error frequency, high-risk operation clearing status, and supplementary training completion records. Based on the preset grading threshold, it automatically determines the student's graduation qualification level; for students who do not meet the standards, it automatically matches targeted special supplementary training courses to complete closed-loop rectification training.

2. The method for standardizing and verifying endoscopic operation training procedures according to claim 1, characterized in that: In step S2, the verification items for the preoperative preparation node include verification of instrument count completeness, preoperative patient assessment process, standardization of patient positioning, and compliance of equipment debugging parameters; the verification items for the standardized operation node of endoscope insertion include verification of insertion depth, endoscope torsion angle, inflation volume control, insertion sequence, and compliance of anti-roughness wall operation; the verification items for the intraoperative cavity observation and treatment operation node include verification of mucosal observation coverage, blind spot ratio, biopsy operation sequence, polyp removal operation sequence, and standardization of hemostasis; the verification items for the endoscope withdrawal observation node include verification of withdrawal speed uniformity, total observation time, completeness of ileocecal re-examination, and completeness of key area re-examination; the verification items for the postoperative debriefing and conclusion node include verification of training record completeness, completion of defect debriefing, submission of mentor evaluation, and generation of supplementary training suggestions.

3. The method for standardizing and verifying endoscopic operation training procedures according to claim 1, characterized in that: In step S3, the multi-dimensional sensing data of the simulated endoscope handle includes endoscope insertion depth data, endoscope torsion angle data, inflation pressure data, and operating force data; in step S4, the operation mechanics compliance verification includes comparing the endoscope insertion depth data, endoscope torsion angle data, inflation pressure data, and operating force data with the node compliance thresholds of the corresponding process nodes to determine whether there are abnormal insertion depths, abnormal endoscope torsion angles, abnormal inflation volume control, abnormal operating force, or abnormal endoscope advance / retreat movements.

4. The method for standardizing and verifying endoscopic operation training procedures according to claim 1, characterized in that: In step S4, the AI ​​recognition and verification of the operation field of view and action sequence includes: performing endoscopic field of view target detection, mucosal missed diagnosis area identification, excessive air injection identification, endoscope body violent push identification, field of view omission identification, and operation sequence inversion identification on real-time video image data under endoscopy, and matching the identified operation actions with the node execution order in the SOP rule base in combination with the time sequence information; when missing actions, inverted action sequence, insufficient observation coverage, excessively rapid endoscope withdrawal, or high-risk operations are identified, real-time error correction prompts are generated and corresponding defect marks are formed.

5. The method for standardizing and verifying endoscopic operation training procedures according to claim 1, characterized in that: In step S4, the structured annotation and verification of teaching defects includes: converting the instructor's voice into text data, performing structured processing on the text data and the instructor's text comment data, extracting defect categories, defect severity and correction suggestions, and associating the defect categories, defect severity and correction suggestions with the operation mechanics compliance verification results, operation field of view and action timing AI recognition verification results of the corresponding process nodes.

6. The method for standardizing and verifying endoscopic operation training procedures according to claim 1, characterized in that: In step S4, the minor defect is used to trigger real-time error correction prompts; the moderate violation is used to be included in the segmented verification score and defect list of the corresponding process node; the fatal high-risk operation error is used to trigger a high-risk warning and serves as a limiting condition in the graduation tiered qualification review and verification.

7. The method for standardizing and verifying endoscopic operation training procedures according to claim 1, characterized in that: In step S6, the graduation qualification level includes the internship operation qualification level, the basic independent operation qualification level, and the minimally invasive treatment operation qualification level. The system determines whether the student has reached the corresponding graduation qualification level for internship operation, basic independent operation, or minimally invasive treatment operation based on the student's full-cycle segmented verification score, error frequency, high-risk operation clearance status, and supplementary training completion record. When the student has not reached the corresponding graduation qualification level, the system automatically matches specialized supplementary training courses related to the standard operation of endoscope insertion, cavity observation, endoscope withdrawal observation, biopsy operation, polyp removal operation, or hemostasis operation according to the type of defect.

8. A standardized verification system for endoscopic operation training procedures, characterized in that, To implement the standardized verification method for endoscopic operation training procedures as described in any one of claims 1 to 7, the system includes a practical data acquisition hardware terminal, a cloud-based intelligent standardized verification platform, and a local teaching management client. The practical data acquisition hardware terminal includes a simulated endoscopic operating handle with multi-dimensional torque and displacement sensors, a digestive tract simulation model, a high-definition endoscopic video acquisition module, an audio acquisition module, and an IoT data transmission module, used to collect trainee practical mechanical data, endoscopic image data, and instructor voice data in real time. The cloud-based intelligent standardized verification platform includes an SOP rule base module, an AI intelligent verification engine module, a hierarchical permission management module, an encrypted file storage module, and an intelligent supplementary training push module. The local teaching management client is deployed on the teaching terminal and is used to realize training course arrangement, dynamic monitoring of practical operation process, real-time violation pop-up reminders, automatic generation of verification reports and export of graduation assessment reports.

9. The standardized verification system for endoscopic operation training procedures according to claim 8, characterized in that: The SOP rule base module has built-in standardized operation thresholds, compliance duration parameters, prohibited operation lists, and high-risk operation judgment criteria for various endoscopic procedures such as gastroscopy, colonoscopy, ERCP, ESD, and EUS, and supports iterative updates of the parameter base according to the residency training guidelines. Among them, ERCP is endoscopic retrograde cholangiopancreatography, ESD is endoscopic submucosal dissection, and EUS is endoscopic ultrasound.

10. The standardized verification system for endoscopic operation training procedures according to claim 8, characterized in that: The AI ​​intelligent verification engine module includes a visual detection unit, a temporal matching unit, a mechanical parameter comparison unit, and a speech NLP structured parsing unit, which are used to realize the fusion analysis of multi-source data and automated compliance judgment; the hierarchical permission management module sets four levels of permissions: student end, instructor end, training base administrator end, and residency training supervision end, which are used to realize practical learning, real-time instructor review, batch student management, and overall training quality supervision and statistics, respectively.

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