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A semantic segmentation-based automatic pterygium assessment and grading system.

Qingbo Ji1,2, Wanyang Liu3,4, Qingfeng Ma1,2

  • 1College of Information and Communication Engineering, Harbin Engineering University, Harbin, China.

Frontiers in Medicine
|March 28, 2025
PubMed
Summary
This summary is machine-generated.

This study introduces an AI system for grading pterygium (eye condition) using deep learning and image analysis. The automated system shows high accuracy and reliability, matching expert ophthalmologist evaluations for precise pterygium assessment.

Keywords:
AI-based diagnosticcurve fittingdeep learningpterygiumsemantic segmentation

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Area of Science:

  • Ophthalmology
  • Artificial Intelligence
  • Medical Image Analysis

Background:

  • Pterygium is a common eye condition requiring accurate severity grading for effective treatment.
  • Limited ophthalmologist resources and a growing patient population necessitate automated diagnostic solutions.
  • Current pterygium evaluation methods can be subjective and time-consuming.

Purpose of the Study:

  • To develop and validate an automated grading system for pterygium severity using artificial intelligence.
  • To combine deep learning and image processing for precise pterygium localization and invasion depth quantification.
  • To provide an efficient and reliable tool for pterygium assessment, aiding clinical decision-making.

Main Methods:

  • A two-module system was developed: semantic segmentation (improved TransUnet) for pterygium localization and curve fitting for invasion depth measurement.
  • The semantic segmentation module was trained on clinical slit-lamp microscope images.
  • The system integrates deep learning with computational methods for comprehensive pterygium analysis.

Main Results:

  • The semantic segmentation model achieved a high Dice coefficient (0.9489 overall, 0.9041 for pterygium).
  • Clinical validation demonstrated excellent grading accuracy (0.9360) and weighted F1 score (0.9363).
  • The system showed strong agreement with expert evaluations (Kappa coefficient: 0.8908), confirming its diagnostic reliability.

Conclusions:

  • The AI-based system accurately automates pterygium grading by integrating semantic segmentation and curve fitting.
  • The developed quantitative evaluation framework aligns closely with expert clinical assessments.
  • This AI tool offers a reliable and efficient solution for pterygium diagnosis, with potential for broader clinical applications.