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[Assistant diagnose for subclinical keratoconus by artificial intelligence].

H H Zou1, J H Xu2, L Zhang1

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[Zhonghua Yan Ke Za Zhi] Chinese Journal of Ophthalmology
|December 26, 2019
PubMed
Summary

Artificial intelligence accurately diagnoses keratoconus and subclinical keratoconus, improving diagnostic efficiency for ophthalmologists. This AI model significantly outperforms human diagnosis in identifying corneal conditions.

Keywords:
Artificial intelligenceDiagnosis, computer-assistedEarly diagnosisKeratoconus

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Area of Science:

  • Ophthalmology
  • Artificial Intelligence in Medicine
  • Corneal Imaging and Diagnostics

Background:

  • Accurate diagnosis of keratoconus and subclinical keratoconus is crucial for preventing vision loss.
  • Traditional diagnostic methods can be subjective and time-consuming, especially for early-stage disease.
  • Artificial intelligence (AI) offers potential for objective and efficient diagnostic tools in ophthalmology.

Purpose of the Study:

  • To develop and evaluate an AI model for diagnosing normal cornea, subclinical keratoconus, and keratoconus.
  • To compare the diagnostic performance of the AI model against human expert and resident labeling.

Main Methods:

  • A diagnostic study involving 2,018 myopic patients aged 18-48 years.
  • Corneal topography data was labeled by experienced ophthalmologists.
  • A Gradient Boosting Decision Tree (GBDT) algorithm was used to analyze 28 corneal parameters, with 80% data for training and 20% for validation. Model performance was assessed using 10-fold cross-validation and receiver operating characteristic (ROC) curves.

Main Results:

  • The AI model achieved an overall diagnostic accuracy of 95.53%, significantly outperforming ophthalmic residents (93.55%).
  • The area under the ROC curve (AUC) for the validation set was 0.9966, indicating high diagnostic capability.
  • Specific accuracies included 96.67% for subclinical keratoconus/normal cornea (AUC 0.9936) and 98.91% for keratoconus/normal cornea (AUC 0.9982).

Conclusions:

  • AI-established models can diagnose subclinical keratoconus with high accuracy.
  • This AI approach significantly enhances diagnostic efficiency and accuracy for ophthalmologists, particularly junior practitioners.
  • The AI model demonstrates superior performance compared to human diagnosis, offering a valuable tool for clinical practice.