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A grading method for Kayser Fleischer ring images based on ResNet.

Wei Song1, Ling Xin1, Jiemei Wang2

  • 1The First Affiliated Hospital of Anhui University of Chinese Medicine, Hefei, 230031, China.

Heliyon
|May 26, 2023
PubMed
Summary
This summary is machine-generated.

This study developed an AI system for detecting and grading Kayser-Fleischer (K-F) rings in Wilson disease (WD) patients. Deep learning models, particularly ResNet34, achieved high accuracy, aiding early WD diagnosis.

Keywords:
HLDK–FResNetWilson's diseaseYOLO algorithm

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Area of Science:

  • Ophthalmology
  • Medical Imaging
  • Artificial Intelligence

Background:

  • Kayser-Fleischer (K-F) rings are a key indicator of Wilson disease (WD).
  • Early detection and grading of K-F rings are crucial for effective WD patient management.
  • Current diagnostic methods for K-F rings can be subjective and time-consuming.

Purpose of the Study:

  • To develop and evaluate deep learning models for automated detection and grading of K-F rings.
  • To create a comprehensive image database of K-F rings for WD patients.
  • To assess the performance of various convolutional neural networks (CNNs) for K-F ring analysis.

Main Methods:

  • Collected and curated a dataset of 1850 K-F ring images from 399 WD patients.
  • Utilized YOLO for initial corneal K-F ring detection and image segmentation.
  • Trained and evaluated deep CNNs (VGG, ResNet, DenseNet) for K-F ring grading on the KFID dataset.

Main Results:

  • ResNet34 achieved the highest recall (95.23%), specificity (96.99%), and F1-score (95.23%).
  • DenseNet demonstrated the best precision (95.66%).
  • Overall accuracies for tested models ranged from 89.88% to 95.31%.

Conclusions:

  • Deep learning models, especially ResNet, are effective for automated K-F ring grading.
  • The developed system shows promise for improving the accuracy and efficiency of WD diagnosis.
  • This AI-driven approach can significantly aid clinicians in diagnosing Wilson disease.