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Multi-Granularity Mask-Guided Network: An Integrated AI Framework for Region-Level Segmentation and Grading of

Yiwen Hu1, Bingyan Hao2,3, Yilin Sun1

  • 1Department of Ophthalmology, Shanghai Tenth People's Hospital, Tongji University School of Medicine, Shanghai 200072, China.

Journal of Clinical Medicine
|April 14, 2026
PubMed
Summary
This summary is machine-generated.

An AI system accurately grades cataracts using AS-OCT images, achieving high agreement with ophthalmologists for cortical and nuclear cataracts and showing promise for posterior subcapsular cataracts.

Keywords:
anterior segment optical coherence tomographyartificial intelligencecataract gradingmulti-granularity feature extraction

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Area of Science:

  • Ophthalmology
  • Artificial Intelligence
  • Medical Imaging

Background:

  • Cataract grading is crucial for diagnosis and treatment planning.
  • Current grading methods can be subjective and time-consuming.
  • Automating cataract grading using advanced imaging techniques is a significant area of research.

Purpose of the Study:

  • To develop and validate an artificial intelligence (AI) system for automated cataract grading.
  • To utilize anterior segment optical coherence tomography (AS-OCT) for grading major cataract subtypes based on the Lens Opacities Classification System III (LOCS III).
  • To assess the performance and interpretability of the proposed AI model.

Main Methods:

  • A single-center cross-sectional study involving AS-OCT image collection and manual grading by ophthalmologists.
  • Development of a novel multi-granularity mask-guided network (MMNet) for joint lens substructure segmentation and severity grading.
  • Independent testing of the MMNet for grading cortical cataract (CC), nuclear cataract (NC), and posterior subcapsular cataract (PSC).

Main Results:

  • The MMNet demonstrated high agreement with gold standard grading, with prediction errors < 1.0 ranging from 83.02% to 89.94%.
  • Cataract subtype grading accuracy ranged from 82.20% to 89.76%, with Area Under the Curve (AUC) values between 0.954 and 0.973.
  • The model achieved satisfactory Mean Absolute Error (MAE) for CC, NC, and PSC, and a grading speed of 0.0178 seconds per image.

Conclusions:

  • The AI model achieved advanced performance in automated LOCS III-based cataract grading for CC and NC using AS-OCT images.
  • The developed AI system shows feasibility for PSC assessment.
  • The MMNet offers a promising tool for objective and efficient cataract grading.