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An Explainable AI Framework for Corneal Imaging Interpretation and Refractive Surgery Decision Support.

Mini Han Wang1,2,3

  • 1Zhuhai People's Hospital (The Affiliated Hospital of Beijing Institute of Technology, Zhuhai Clinical Medical College of Jinan University), Zhuhai 519000, China.

Bioengineering (Basel, Switzerland)
|November 27, 2025
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Summary

This study presents an AI framework using neuro-symbolic reasoning and large language models (LLMs) for corneal analysis. It accurately detects early keratoconus and aids refractive surgery decisions, offering explainable, bilingual reports.

Keywords:
corneal bioengineeringcorneal topographyexplainable artificial intelligence (XAI)large language model (LLM)neuro-symbolic techniquessurgical decision support

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Area of Science:

  • Ophthalmology
  • Artificial Intelligence
  • Medical Imaging

Background:

  • Corneal topography analysis is crucial for diagnosing ectatic diseases like keratoconus and planning refractive surgery.
  • Current interpretation methods can be complex and time-consuming, necessitating advanced decision support tools.

Purpose of the Study:

  • To develop and validate an explainable neuro-symbolic and large language model (LLM)-driven framework for corneal topography interpretation.
  • To provide intelligent decision support for early keratoconus detection and refractive surgery planning.

Main Methods:

  • A four-stage pipeline involving automated parameter extraction, knowledge graph mapping, Bayesian inference, and LLM-based reporting (DeepSeek, GPT-4.0).
  • Prospective analysis of 20 eyes using IOLMaster 700 data.
  • Benchmarking against senior corneal specialists and baseline Convolutional Neural Network (CNN) and Vision Transformer (ViT) models.

Main Results:

  • The framework achieved high diagnostic performance for early keratoconus detection (92-94% sensitivity/specificity, AUC 0.95).
  • It demonstrated strong performance for refractive surgery eligibility (F1 score 0.90).
  • Generated bilingual reports were highly rated for clarity and clinical utility, with outputs generated rapidly (~95 seconds).

Conclusions:

  • The neuro-symbolic and LLM-driven framework offers a transparent, rapid, and clinically actionable AI solution for ophthalmic practice.
  • This approach significantly enhances early ectatic disease detection and supports individualized refractive surgery planning.
  • The integration of explainable AI (XAI) principles ensures trustworthy and interpretable clinical decision support.