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Design and application of a web-based intelligent ophthalmic image analysis teaching platform.
Junyi Chen1, Yang Yang1, Yuanzhuo Song2
1School of Medical Information and Engineering, Southwest Medical University, Luzhou, China.
Frontiers in Medicine
|May 11, 2026
Summary
A new web-based platform enhances ophthalmic image analysis training by integrating artificial intelligence (AI) into clinical workflows. This tool significantly improves students' theoretical and practical competence in AI-driven ophthalmic education.
Area of Science:
- Ophthalmology
- Medical Education
- Artificial Intelligence
Background:
- The integration of artificial intelligence (AI) into ophthalmology necessitates interdisciplinary education combining clinical image interpretation with AI methods.
- Existing structured teaching platforms for ophthalmic image analysis training are limited.
- There is a need for effective educational tools to bridge the gap between clinical practice and AI in ophthalmology.
Purpose of the Study:
- To design and evaluate a web-based intelligent ophthalmic image analysis teaching platform.
- To assess the usability and educational effectiveness of the developed platform.
- To provide a comprehensive training solution for ophthalmic image analysis using AI.
Main Methods:
- Development of a modular web-based platform supporting fundus photography and OCT analysis workflows.
- Implementation of image preprocessing, model training, performance evaluation, and AI-assisted guidance features.
- Usability assessment via a questionnaire survey (n=121) using the System Usability Scale (SUS) and competency ratings.
- A controlled teaching experiment with 64 third-year undergraduate students comparing the platform to traditional instruction.
Main Results:
- The platform successfully integrated clinical image cognition and AI-driven analysis.
- The median SUS score was 80.0, significantly exceeding the benchmark of 68 (p < 0.001).
- Self-assessed competency scores ranged from 4.31 to 4.53 (out of 5), indicating high competence.
- Students using the platform showed significantly higher scores in theoretical, practical, and comprehensive competence compared to the control group (p < 0.05).
Conclusions:
- The web-based intelligent ophthalmic image analysis teaching platform demonstrates high usability and educational benefits.
- Integrating AI workflows into ophthalmic education enhances competency development.
- The platform serves as a valuable tool for intelligent ophthalmic education and training.
