A method and system for simulating visual conduction pathway impairment

By simulating visual transmission path damage, student operation data is collected and analyzed to achieve refined diagnosis and adaptive training of accidental touch patterns. This solves the problems of inaccurate positioning and rough feedback in existing technologies, and improves the accuracy and efficiency of teaching and training.

CN121565040BActive Publication Date: 2026-04-14JIANGNAN UNIV +1
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Patent Information

Application Number
CN202610090600.4
Authority / Receiving Office
CN · China
Patent Type
Patents(China)
Current Assignee / Owner
Filing Date
2026-01-23
Publication Date
2026-04-14
Estimated Expiration
2046-01-23

AI Technical Summary

Technical Problem

Existing teaching and training methods for visual transmission pathways rely on two-dimensional diagrams and static anatomical models, which make it difficult to achieve high-precision interactive operation training. The positioning accuracy is easily affected by environmental factors, the operation feedback is rough, and quantitative analysis and targeted guidance are not possible, resulting in poor training effects.

Method used

A simulation method for visual transmission path damage is adopted. By collecting student operation data, data stability assessment, cluster analysis and misjudgment rate calculation are performed to establish the correlation between clusters and damaged nodes, so as to realize the refined diagnosis of mis-touch patterns and adaptive adjustment of training content.

Benefits of technology

It improves the reliability and effectiveness of teaching and training, enables personalized teaching through quantitative feedback, and enhances the accuracy of operational error diagnosis and training efficiency.

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Abstract

The present application relates to the field of medical education simulation training, more particularly, the present application relates to a visual conduction pathway injury simulation method and system, the method comprises: collecting the positioning data of students on the model containing multiple injury nodes, calculating the stability degree by comprehensively evaluating the data volatility and spatial weight, and screening out effective positioning data; dynamic clustering analysis is carried out on the effective data, the mis-touch mode is identified and mapped to the corresponding injury node; the misdiagnosis rate of each node is calculated based on the correct identification times and mis-touch mode information, and the teaching difficulties are identified and the training content is adaptively adjusted. The present application solves the problems of low interaction precision and rough evaluation of existing teaching tools through data reliability processing, mis-touch mode fine analysis and quantitative feedback closed loop, and significantly improves the data reliability, error diagnosis accuracy and personalized teaching effect of simulation training.
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