Anesthesia clinical ability-oriented perioperative ultrasound hierarchical teaching evaluation method

Abstract

The application discloses a perioperative ultrasonic layered teaching evaluation method for anesthesia clinical ability, and relates to the technical field of medical education. The method constructs a training evaluation framework of "three layers, three levels and three scenes", divides into three training levels of an introductory layer, an advanced layer and a proficient layer according to the basis of the trainee, covers three perioperative scenes of preoperative evaluation, intraoperative guidance and postoperative analgesia, sets three evaluation dimensions of operation skill, image interpretation and clinical decision, generates objective scores through a multi-dimensional quantitative evaluation model combined with ultrasonic image automatic recognition technology, and dynamically adjusts a teaching path according to the evaluation results to form a teaching-evaluation-feedback closed loop. The application solves the problems of the absence of traditional anesthesia ultrasonic teaching stratification, the subjectivity of evaluation and the insufficiency of feedback, can significantly improve the teaching adaptability and the objectivity of evaluation, shorten the training cycle, and adapt to the standardized training needs of anesthesiology resident physicians.

Description

Technical Field

[0001] This invention belongs to the field of medical education technology, and in particular relates to a perioperative ultrasound stratified teaching assessment method for anesthesia clinical competence. Background Technology

[0002] Perioperative ultrasound, as one of the core clinical skills of anesthesiologists, can be applied to multiple scenarios such as preoperative airway and vascular volume assessment, intraoperative nerve block and circulation monitoring, postoperative analgesia and complication screening, and is a key technology to improve anesthesia safety and treatment quality.

[0003] In the current standardized training system for anesthesiologists in China, ultrasound skills training mostly adopts the traditional teaching model, where instructors arrange teaching content based on their personal experience. There is a lack of unified tiered training standards, and trainees with different backgrounds receive homogeneous teaching. This results in trainees with weak foundations falling behind, while trainees with better foundations repeatedly learn inefficient content. The overall teaching cycle is long and the training quality is uneven.

[0004] In the assessment phase, existing assessments mostly adopt a final evaluation method of a single practical operation plus a written test. The assessment scenarios are concentrated on single nerve block or vascular puncture projects, which do not cover the application scenarios of the entire perioperative process and cannot fully reflect the comprehensive ability of resident trainees in real clinical work.

[0005] Meanwhile, the existing assessments rely heavily on the subjective judgment of the supervising teachers. The scoring standards of different supervising teachers vary greatly, resulting in insufficient reliability and validity of the assessment results. They are unable to objectively reflect the true skill level of the trainees and cannot accurately identify their skill gaps.

[0006] Furthermore, the existing teaching model lacks a linkage mechanism between assessment and teaching. The assessment results are only used for grade determination and do not guide the adjustment of teaching content. Trainees cannot receive targeted reinforcement training, the teaching feedback loop is missing, and the efficiency of skill improvement is limited.

[0007] In recent years, although some digital teaching platforms have introduced ultrasound teaching materials and simulated assessments, most of them have only realized the online transmission of teaching content. They have not designed differentiated training paths based on the levels of standardized training trainees, nor have they established multi-dimensional quantitative assessment models, making it difficult to meet the needs of standardized training of anesthesiology clinical skills. Summary of the Invention

[0008] This invention addresses the problems in existing technologies by providing a perioperative ultrasound-based stratified teaching and assessment method for anesthesia clinical competence. This method constructs a "three-layer, three-level, three-scenario" training and assessment framework, combines a quantitative assessment model with a dynamic advancement mechanism, and achieves precise, progressive, and objective comprehensive competence assessment through stratified teaching.

[0009] To solve the above-mentioned technical problems, the technical solution adopted by the present invention is as follows:

[0010] A perioperative ultrasound stratified teaching assessment method for anesthesia clinical competence includes the following steps:

[0011] Step 1: Construct a tiered teaching system. Divide the trainees into different training levels based on their basic knowledge of anesthesia ultrasound and years of clinical experience. Match each level with a corresponding perioperative ultrasound teaching module and assessment standards.

[0012] Step 2: Construct a perioperative multi-scenario assessment index system covering three clinical scenarios: preoperative assessment, intraoperative guidance, and postoperative analgesia. For each scenario, set up three-level assessment dimensions: operational skills, image interpretation, and clinical decision-making, and configure corresponding quantitative scoring items for each dimension.

[0013] Step 3: Collect ultrasound operation data, image interpretation and clinical decision-making data of trainees in the teaching assessment, input them into the preset multi-dimensional quantitative assessment model, and calculate the comprehensive ability score and sub-item score of each dimension of the trainees.

[0014] Step four: Based on the comprehensive ability score and the sub-item score results, match the corresponding level of advancement rules, dynamically adjust the teaching module content and assessment difficulty of the next stage for the trainees, and form a complete closed loop of teaching-assessment-feedback.

[0015] Furthermore, the cultivation levels in step one specifically include:

[0016] The introductory level corresponds to the first-year resident physicians in the anesthesiology major, and the teaching modules mainly focus on basic ultrasound theory and standard section scanning.

[0017] The advanced level corresponds to the second-year resident physicians in the anesthesiology specialty, and the teaching modules mainly focus on ultrasound assessment and intraoperative guidance for common diseases;

[0018] The advanced level corresponds to third-year resident physicians in the anesthesiology major, and the teaching modules mainly focus on ultrasound decision-making and complication management in complex cases.

[0019] Furthermore, the three types of clinical scenarios in step two specifically include preoperative assessment scenarios, corresponding to preoperative airway assessment, volume assessment, and circulatory system assessment items;

[0020] Intraoperative guidance scenarios correspond to nerve block guidance, vascular puncture guidance, and cardiopulmonary function monitoring; postoperative analgesia scenarios correspond to postoperative analgesia block localization and postoperative complication assessment.

[0021] Furthermore, in the three-level assessment dimensions of step two, the scoring items for the operational skills dimension include scanning time, standard section matching degree, operational standardization, and image clarity; the scoring items for the image interpretation dimension include anatomical structure recognition accuracy, abnormal sign recognition accuracy, and measurement error rate; and the scoring items for the clinical decision-making dimension include the rationality of the diagnostic conclusion, the suitability of the treatment plan, and the accuracy of risk prediction.

[0022] Furthermore, the multi-dimensional quantitative evaluation model in step three uses a weighted summation method to calculate the comprehensive score, where the operational skills dimension has a weight of 35%, the image interpretation dimension has a weight of 35%, and the clinical decision-making dimension has a weight of 30%. Each scoring item under each dimension has a different score percentage according to its training level.

[0023] Furthermore, step three also includes an automatic ultrasound image recognition step, which uses a pre-trained convolutional neural network model to perform standard section recognition and anatomical structure annotation on ultrasound images collected from trainees, and automatically generates objective scores for image clarity and recognition accuracy.

[0024] Furthermore, the advancement rules in step four are as follows: when a trainee's overall score in two consecutive assessments reaches or exceeds the passing score for the current level and there are no unsatisfactory sub-items, the trainee is automatically promoted to the next level of teaching module; when a trainee's overall score in a single assessment is below the passing score or there are two or more unsatisfactory sub-items, the trainee is automatically reverted to the reinforcement teaching module for the corresponding weak item.

[0025] Furthermore, the method also includes a mentor management step, through which mentors can adjust the scoring weights and assessment question banks for each level, view the overall ability distribution and weakness statistics of resident trainees, and generate teaching quality analysis reports in batches.

[0026] The beneficial effects of the present invention after adopting the above structure are as follows:

[0027] (1) A three-tiered teaching system adapted to the growth pattern of anesthesiologists was constructed. Different teaching content and assessment standards were set for trainees with different backgrounds, avoiding the drawbacks of homogeneous teaching. This ensured that trainees with weak foundations could solidify their basic skills, while also meeting the ability improvement needs of high-level trainees.

[0028] (2) An assessment scenario covering the entire perioperative period before, during and after surgery was established. Combining the three-level assessment dimensions of operational skills, image interpretation and clinical decision-making, it breaks the limitations of traditional assessment of single skills and single scenarios, and the assessment results are more in line with the requirements of real clinical work.

[0029] (3) A multi-dimensional quantitative evaluation model was adopted, and an objective operation score was generated by combining artificial intelligence image recognition technology. This reduced the scoring bias caused by the subjective judgment of the supervising teacher, improved the reliability and validity of the evaluation results, and made the assessment results of different supervising teachers and different batches comparable.

[0030] (4) A dynamic feedback mechanism for advancement and regression has been set up, and the teaching path is automatically adjusted according to the assessment results, realizing a complete closed loop of teaching-assessment-feedback. It can accurately strengthen the weak points of the trainees and effectively shorten the skill attainment period of the trainees.

[0031] (5) The method has good scalability. By adjusting the teaching modules and evaluation indicators, it can be quickly transferred to ultrasound skills teaching in other departments such as surgery and emergency, and can also be applied to standardized training scenarios for other clinical operation skills. Attached Figure Description

[0032] The accompanying drawings are provided to further illustrate the invention and form part of the specification. They are used together with the embodiments of the invention to explain the invention and do not constitute a limitation thereof.

[0033] Figure 1 This is a flowchart illustrating the overall process of the present invention.

[0034] Figure 2 This is a three-level hierarchical logic diagram for resident trainees in this invention;

[0035] Figure 3 This is a breakdown diagram of the evaluation indicators of the present invention;

[0036] Figure 4 This is a data flow diagram of the AI ​​quantitative scoring method of the present invention;

[0037] Figure 5 This is the dynamic progression logic diagram of the present invention;

[0038] Figure 6 This is a diagram of the overall system platform architecture of the present invention. Detailed Implementation

[0039] The technical solution of the present invention will be further described in detail below with reference to specific embodiments, but the scope of protection of the present invention is not limited to the following embodiments.

[0040] This method is implemented based on the digital platform for standardized training of anesthesiologists. The platform includes a resident physician end, a teaching staff end, and a back-end management end. It connects to ultrasound simulation training equipment and a clinical case database, and integrates an ultrasound image recognition module based on convolutional neural networks, which can automatically collect operational data and generate objective scores.

[0041] Example 1: Implementation of Teaching Assessment for Entry-Level Resident Trainees

[0042] This example is for first-year resident physicians who have just entered the Department of Anesthesiology. It belongs to the introductory training system. The training objective is to master the basic theory of perioperative ultrasound and the skills of commonly used standard section scanning, and to be able to identify normal anatomical structures and complete basic measurements.

[0043] The introductory teaching modules specifically include: a basic ultrasound theory module, covering ultrasound imaging principles, ultrasound instrument operation, and safety regulations; a standard section scanning module, covering scanning operations of commonly used perioperative sections such as neck blood vessels, brachial plexus, and cardiopulmonary basic sections; and a basic image interpretation module, covering identification of normal anatomical structures and basic measurement methods.

[0044] The introductory level assessment is set up in a single scenario of preoperative evaluation, focusing on operational skills and image interpretation ability. The clinical decision-making dimension only has basic questions. The score distribution of each dimension is as follows: operational skills 40%, image interpretation 40%, and clinical decision-making 20%.

[0045] The assessment process is as follows: trainees complete a standard section scan of the right internal jugular vein on an ultrasound simulation device. The system automatically records the scan duration, saves the operation process video and the final section image. Trainees annotate the anatomical structures and measure the blood vessel diameter in the system. Trainees complete three basic clinical scenario multiple-choice questions to determine the clinical applicable scenario corresponding to the section.

[0046] The system collected the following operational data: scanning time 42 seconds, feature point matching degree of the final cross-section with the standard cross-section 92%, no violations during the operation, and image clarity score of 88 points.

[0047] The image interpretation data includes: 4 out of 5 anatomical structures were correctly identified, with an accuracy rate of 80%; the measured blood vessel diameter had an error of 0.3 mm compared to the standard value, with an error rate of 2.1%.

[0048] In the clinical decision-making section, 2 out of 3 questions were answered correctly, achieving an accuracy rate of 66.7%.

[0049] The scores for each item were calculated using a quantitative assessment model. The scores for the operational skills dimension were 89.2, the image interpretation dimension was 81.5, and the clinical decision-making dimension was 66.7. The weighted overall score was 80.1.

[0050] The passing score for the entry level is 70 points. If a trainee's overall score meets the standard, but the score in the clinical decision-making dimension is lower than the passing score of 70 points, the system will automatically push the clinical decision-making foundation reinforcement module to them, which includes scenario training with 10 basic cases. Only after completing the reinforcement training can they enter the next round of comprehensive assessment.

[0051] In this embodiment, the convolutional neural network model used for image recognition is trained using 12,000 annotated ultrasound images of the neck vessels, including 9,600 training images and 2,400 validation images. After training, the accuracy rate of standard section recognition can reach 94.7%, and the accuracy rate of anatomical structure annotation can reach 91.2%, which can replace the teaching staff to complete most of the objective indicator scoring.

[0052] Example 2: Implementation of Teaching Assessment for Advanced Resident Trainees

[0053] This example is for second-year resident physicians with one year of clinical experience in anesthesia, belonging to the advanced training system. The training objective is to master the perioperative ultrasound assessment methods for common diseases, be able to independently perform routine nerve blocks and vascular puncture guidance, and have basic clinical decision-making ability.

[0054] The advanced teaching modules specifically include: a common disease ultrasound assessment module, covering ultrasound assessment of common clinical problems such as difficult airway, volume status, and heart failure; an intraoperative guidance operation module, covering ultrasound guidance for routine operations such as brachial plexus block, femoral nerve block, and central venous puncture; and a clinical decision-making thinking module, covering the corresponding logic of ultrasound result interpretation and anesthesia plan adjustment.

[0055] The advanced level assessment covers two scenarios: preoperative assessment and intraoperative guidance. The score distribution of the three dimensions is as follows: operational skills account for 35%, image interpretation accounts for 35%, and clinical decision-making accounts for 30%.

[0056] The assessment was conducted in the form of a simulated case, with the patient being a 65-year-old patient scheduled for elective abdominal surgery. The patient presented with suspected difficult airway and hypovolemia preoperatively. Trainees were required to complete the preoperative ultrasound assessment and develop an anesthesia plan, and to simulate the internal jugular vein puncture and guidance procedure during the operation.

[0057] During the assessment, the system automatically collected operational data. The preoperative ultrasound assessment completed three standard sectional scans, with an average single-sectional scan time of 58 seconds. The sectional matching rates were 90%, 88%, and 93%, respectively. There was one deduction for operational standardization, which was due to improper probe pressure control. The average image clarity score was 85 points.

[0058] In the image interpretation section, a total of 8 anatomical structures and abnormal signs were identified, with 6 correct identifications, achieving an accuracy rate of 75%. The average error rate of the three measurement indicators was 3.2%.

[0059] The clinical decision-making section includes three subjective questions: diagnosis, adjustment of anesthesia plan, and risk prediction. The system scores the questions by combining keyword matching with review by the supervising teacher, and the final score is 78 points.

[0060] According to the quantitative model calculation, the operational skills dimension scored 82.7 points, the image interpretation dimension scored 76.3 points, the clinical decision-making dimension scored 78 points, and the overall score was 79.1 points.

[0061] The passing score for the advanced level is 75 points. This trainee's overall score meets the standard and all sub-scores are above the passing score. Combined with the previous assessment score of 76.8 points, the trainee has passed the assessment twice in a row. The system automatically triggers the advanced process, pushes the pre-learning module for the proficiency level, and prompts the supervising teacher to pay attention to further improving the trainee's image interpretation ability.

[0062] Example 3: Implementation of Teaching Assessment for Proficiency Level Trainees

[0063] This embodiment is designed for third-year resident physicians with more than two years of clinical experience in anesthesia. They belong to the proficiency level training system, and the training goal is to enable them to handle perioperative ultrasound issues in complex and critical cases and to have independent clinical decision-making and complication management capabilities.

[0064] The advanced teaching modules specifically include: a complex and critical case ultrasound assessment module, covering rapid ultrasound assessment of critical scenarios such as shock, acute heart failure, pulmonary edema, and hemopneumothorax; a complication ultrasound identification and management module, covering ultrasound identification and management of nerve block complications, puncture complications, and perioperative cardiopulmonary complications; and a multimodal integrated decision-making module, covering methods for integrating and analyzing ultrasound results with other monitoring data.

[0065] The proficiency level assessment covers three complete perioperative scenarios: preoperative assessment, intraoperative guidance, and postoperative analgesia. It adopts a comprehensive case assessment format, setting the scenario as a 72-year-old emergency intestinal obstruction surgery patient with coronary heart disease and COPD, who showed signs of shock before surgery. The trainees are required to complete the entire process of ultrasound assessment and decision-making.

[0066] The three dimensions are weighted as follows: operational skills account for 25%, image interpretation accounts for 35%, and clinical decision-making accounts for 40%, emphasizing the assessment of clinical decision-making ability.

[0067] The assessment data collection results are as follows: In the operational skills dimension, a total of 6 ultrasound scanning operations were completed, with the average time meeting the requirements, the average cross-sectional matching rate being 89%, and the operation being standardized with no obvious errors, resulting in a score of 84.5. In the image interpretation dimension, 12 anatomical and abnormal signs were identified, with 10 correct, an accuracy rate of 83.3%, and an average measurement error rate of 2.7%, resulting in a score of 82.1. In the clinical decision-making dimension, 4 comprehensive questions were included, covering diagnosis, treatment, complication prediction, and treatment plan adjustment. The scores were averaged after blind evaluation by 3 instructors with associate professor or higher titles, resulting in a score of 86.

[0068] After weighted calculation, the overall score was 84.3 points, reaching the passing score of 80 points for the proficiency level. The system determined that the trainee had the ability to independently complete perioperative ultrasound work, generated a graduation assessment report, and marked the trainee's strength as clinical decision-making and weakness as stability in complex slicing operations, for reference in subsequent continuing education.

[0069] Control examples and effect verification

[0070] To objectively verify the effect of this tiered teaching assessment method on improving the perioperative ultrasound skills of anesthesiologists in residency training and to eliminate the learning time confounding bias caused by simple sequential training, this study adopted a randomized two-stage crossover controlled trial to verify the effect, strictly adhering to the 12-month anesthesiology rotation cycle of first-year anesthesiologists.

[0071] 1. Research subjects and grouping

[0072] Sixty first-year resident physicians from the same batch of anesthesiology department of a tertiary hospital were selected as the research subjects and randomly divided into two groups, A and B, with 30 people in each group. All subjects rotated through the anesthesiology department for a uniform period of 12 months. The study was divided into two intervention phases: the first intervention phase (months 1-6) and the second intervention phase (months 7-12). A two-week standardized ultrasound basic washout transition period was set between the two phases (standardized basic section review teaching was carried out to eliminate skill bias bias caused by the previous teaching model).

[0073] Intervention order random cross-assignment:

[0074] Group A will use the traditional mentoring model from month 1 to month 6, and the tiered teaching and assessment method of this invention from month 7 to month 12.

[0075] Group B adopted the tiered teaching assessment method of this invention from the first to the sixth month, and the traditional mentoring model from the seventh to the twelfth month.

[0076] The two groups had the same total intervention duration, with each participant receiving 6 months of each of the two teaching modes within a 12-month rotation.

[0077] 2. Experiment Implementation and Blinded Evaluation

[0078] Both intervention phases lasted for 6 months. After each phase, a standardized perioperative ultrasound comprehensive assessment was conducted. The assessment was conducted blinded throughout, with three uninformed anesthesiologists with associate senior or higher titles scoring independently and the average score was taken. Simultaneously, three types of observation indicators were collected: the average time for each group of trainees to achieve the skill standard, the intragroup correlation coefficient (ICC) of teacher scores, and the trainees' teaching satisfaction questionnaire. Throughout the trial, a universal online ultrasound question bank and ultrasound simulation training equipment were made available to all trainees without discrimination. At the same time, a monthly free Q&A clinic for weak areas was set up to ensure that trainees had access to supplementary learning channels on their own.

[0079] 3. Ethical Compliance Statement

[0080] This cross-comparison design mitigates the risk of harm to students' rights from multiple dimensions:

[0081] First, all trainees are equally allocated training time for both teaching modes, with the only difference being the order of intervention. There is no situation where a single trainee receives inefficient traditional teaching throughout the entire process, and the allocation of teaching resources is completely balanced.

[0082] Secondly, the two-week transition period between stages can smooth out the shortcomings of the early teaching. For Group A students who received traditional teaching first, the pre-requisite knowledge points of the tiered teaching can be supplemented simultaneously. The new tiered teaching in the second half can make up for the weak skills in the early stage. All students can reach the same standardized training skills standard when they complete the 12-month rotation and will not affect the standardized training graduation assessment.

[0083] Third, the experiment will not adjust the mandatory requirements for completing the residency training. Trainees can voluntarily sign an informed consent form and have the right to withdraw unconditionally. After withdrawal, the original standardized training arrangements will not be changed and there will be no loss of academic or professional rights.

[0084] Fourth, providing free supplementary teaching resources throughout the process serves as protective compensation, eliminating learning gaps caused by different teaching stages, and avoiding ethical controversies.

[0085] 4. Data Statistics and Experimental Results

[0086] Using cross-experiment-specific ANOVA to separate the teaching intervention effect, time-series learning effect, and between-group random error, the source of skill improvement was accurately identified. Statistical results showed that the average comprehensive assessment score of students in the intervention stage using this stratified teaching assessment method was 85.2 points, while the average score in the traditional mentoring intervention stage was 72.3 points. The assessment results of the new teaching model were significantly better.

[0087] In terms of the time required to reach the required standards, the average time for students to reach the corresponding level qualification standard under the tiered teaching model is 4.2 months, while the average time under the traditional model is 5.8 months, shortening the training cycle by approximately 27.6%.

[0088] In terms of scoring consistency, the ICC value of teachers' scores under the new quantitative assessment system is 0.89, while that of the traditional model is only 0.62, which significantly reduces subjective scoring bias.

[0089] In terms of student satisfaction, the satisfaction rates for teaching suitability, assessment objectivity, and feedback relevance in the tiered teaching stage reached 93.3%, 90.0%, and 96.7%, respectively, significantly higher than the 66.7%, 56.7%, and 63.3% in the traditional teaching stage.

[0090] In summary, the perioperative ultrasound stratified teaching and assessment method for anesthesia clinical competence described in this invention can effectively improve the pertinence and objectivity of anesthesia ultrasound teaching and assessment, shorten the talent training cycle, and improve the training quality. It is suitable for promotion and application in the standardized training of anesthesiologists.

[0091] Although embodiments of the present invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions, and variations 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. In conclusion, if those skilled in the art, inspired by this description, design similar structural methods and embodiments without departing from the inventive spirit of the invention, such designs should fall within the protection scope of the present invention.

Claims

1. A perioperative ultrasound stratified teaching and assessment method for clinical anesthesia competence, characterized in that: Includes the following steps: Step 1: Construct a tiered teaching system, dividing trainees into different training levels based on their basic knowledge of anesthesia ultrasound and years of clinical experience, and matching each level with corresponding perioperative ultrasound teaching modules and assessment standards. Step 2: Construct a perioperative multi-scenario assessment index system covering three clinical scenarios: preoperative assessment, intraoperative guidance, and postoperative analgesia. For each scenario, set up three-level assessment dimensions: operational skills, image interpretation, and clinical decision-making, and configure corresponding quantitative scoring items for each dimension. Step 3: Collect ultrasound operation data, image interpretation answer data, and clinical decision answer data of trainees in the teaching assessment, input them into the preset multi-dimensional quantitative assessment model, and calculate the trainees' comprehensive ability score and sub-item scores of each dimension; Step four: Based on the comprehensive ability score and the sub-item score results, match the corresponding level's advancement rules, dynamically adjust the teaching module content and assessment difficulty for the next stage of students, and form a complete closed loop of teaching-assessment-feedback.

2. The perioperative ultrasound stratified teaching and assessment method for anesthesia clinical competence according to claim 1, characterized in that: The training levels in step one specifically include the beginner level, intermediate level, and expert level; The introductory level corresponds to the first-year resident physicians in the anesthesiology major, and the teaching modules mainly focus on basic ultrasound theory and standard section scanning. The advanced level corresponds to the second-year resident physicians in the anesthesiology specialty, and the teaching modules mainly focus on ultrasound assessment and intraoperative guidance for common diseases; The advanced level corresponds to third-year resident physicians in the anesthesiology major, and the teaching modules mainly focus on ultrasound decision-making and complication management in complex cases.

3. The perioperative ultrasound stratified teaching and assessment method for anesthesia clinical competence according to claim 1, characterized in that: The three clinical scenarios in step two specifically include: preoperative assessment scenario, corresponding to preoperative airway assessment, volume assessment, and circulatory system assessment; intraoperative guidance scenario, corresponding to nerve block guidance, vascular puncture guidance, and cardiopulmonary function monitoring; and postoperative analgesia scenario, corresponding to postoperative analgesia block localization and postoperative complication assessment.

4. The perioperative ultrasound stratified teaching and assessment method for anesthesia clinical competence according to claim 1, characterized in that: In the three-level assessment dimensions of step two, the scoring items for the operational skills dimension include scanning time, standard section matching degree, operational standardization, and image clarity; the scoring items for the image interpretation dimension include anatomical structure recognition accuracy, abnormal sign recognition accuracy, and measurement error rate; and the scoring items for the clinical decision-making dimension include the rationality of the diagnostic conclusion, the suitability of the treatment plan, and the accuracy of risk prediction.

5. The perioperative ultrasound stratified teaching and assessment method for anesthesia clinical competence according to claim 1, characterized in that: The multi-dimensional quantitative assessment model in step three uses a weighted summation method to calculate the comprehensive score, with the operational skills dimension having a weight of 35%, the image interpretation dimension having a weight of 35%, and the clinical decision-making dimension having a weight of 30%. Each scoring item under each dimension has a different score percentage according to its training level.

6. The perioperative ultrasound stratified teaching and assessment method for anesthesia clinical competence according to claim 1, characterized in that: Step three also includes an automatic ultrasound image recognition step, which uses a pre-trained convolutional neural network model to perform standard cross-sectional recognition and anatomical structure annotation on the ultrasound images collected by the trainees, and automatically generates an objective score for image clarity and recognition accuracy.

7. The perioperative ultrasound stratified teaching and assessment method for anesthesia clinical competence according to claim 1, characterized in that: The advancement rules in step four are as follows: when a student's overall score in two consecutive assessments reaches or exceeds the passing score for the current level and there are no unqualified sub-items, the student is automatically promoted to the next level of teaching module. When a student's overall score in a single assessment is below the passing score or there are two or more unqualified sub-items, the student is automatically relegated to the reinforcement teaching module for the corresponding weak item.

8. The perioperative ultrasound stratified teaching and assessment method for anesthesia clinical competence according to claim 1, characterized in that: The method also includes a mentor management step, through which mentors can adjust the scoring weights and assessment question banks for each level, view the overall ability distribution and weakness statistics of resident trainees, and generate teaching quality analysis reports in batches.

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