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A deep learning-driven cataract screening model derived from multicenter real-world dataset.

Zhonghui Cui1, Yu Cheng2, Siqi Pan2

  • 1Department of Ophthalmology, The Second Affiliated Hospital of Anhui Medical University, Hefei, China.

Frontiers in Medicine
|December 15, 2025
PubMed
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This study developed a robust artificial intelligence model for cataract screening using diverse, real-world data from multiple centers. The AI tool shows high accuracy, improving generalizability for widespread ophthalmic screening and blindness prevention.

Area of Science:

  • Ophthalmology
  • Artificial Intelligence
  • Medical Imaging

Background:

  • Cataracts are the primary cause of reversible blindness globally, necessitating early detection.
  • Existing AI models for cataract screening often lack generalizability due to homogeneous, single-center training data.

Purpose of the Study:

  • To develop and validate a generalizable deep learning model for cataract detection using a large-scale, multicenter dataset.
  • To establish a methodological framework for trustworthy medical AI systems.

Main Methods:

  • A deep learning model was trained on 22,094 slit-lamp images from 21 institutions across China.
  • A cascaded framework was employed, including automated quality assessment, confounder screening (e.g., pterygium), and differential diagnosis.
  • Various deep learning architectures were evaluated within this framework.
Keywords:
cataract diagnosisdeep learningmedical image analysismulticenter datareal-world study

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Main Results:

  • The leading models achieved high performance on an independent test set for cataract classification.
  • A ResNet50-IBN model demonstrated 93.74% accuracy, 97.74% specificity, and 95.30% AUC.
  • The model proved robust and generalizable due to multicenter, real-world data training.

Conclusions:

  • Training AI models on diverse, multicenter data enhances robustness and generalizability for ophthalmic screening.
  • The developed model serves as a reliable tool for large-scale screening and prevention of blindness.
  • This study provides a blueprint for creating trustworthy medical deep learning systems.