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Updated: May 9, 2026

Subjective Refraction Test Using a Smartphone for Vision Screening
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Smartphone-based lightweight AI system for real-time multiple anterior segment disease screening: development and

Yuwen Liu1, Changsheng Xu1,2, Shinan Wu1

  • 1Fujian Provincial Key Laboratory of Ophthalmology and Visual Science, Xiamen University affiliated Xiamen Eye Center, Fujian Engineering and Research Center of Eye Regenerative Medicine, Eye Institute of Xiamen University, School of Medicine of Xiamen University, Xiamen, Fujian, China.

BMC Medicine
|May 7, 2026
PubMed

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Summary

A new AI system, the Intelligent Detection System (IDS), enables smartphone-based screening for anterior segment diseases. This technology offers efficient, multi-disease detection, improving access to eye care globally.

Area of Science:

  • Ophthalmology
  • Artificial Intelligence
  • Medical Imaging

Background:

  • Anterior segment diseases are a leading cause of preventable blindness globally.
  • Diagnosis is hindered by the need for specialized equipment like slit-lamp biomicroscopy, limiting access in primary care.
  • Existing AI solutions lack efficiency and generalizability for widespread mobile deployment and multi-disease screening.

Purpose of the Study:

  • To develop a smartphone-compatible AI platform for real-time, automated, multi-disease screening of anterior segment diseases.
  • To create a lightweight deep learning model optimized for standard smartphone images.
  • To enable scalable, multi-disease screening platforms accessible via mobile devices.

Main Methods:

  • Developed the Intelligent Detection System (IDS) with a novel, lightweight deep learning model (Eye-YOLO).
Keywords:
Anterior segment imageArtificial intelligenceDeep learningOphthalmic screening

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  • Trained the model on a diverse dataset of 24,671 slit-lamp and smartphone images from multiple centers.
  • Integrated image quality assessment and urgency classification.
  • Prospectively validated IDS via a community screening WeChat Mini Program.

    • Main Results:

    • Eye-YOLO achieved high performance (0.816 mAP) with a compact architecture, enabling real-time mobile inference (131 FPS).
    • IDS demonstrated high diagnostic accuracy (93.50%) and AUC (0.9837) across various smartphone brands.
    • AI assistance significantly improved junior ophthalmologists' diagnostic accuracy.
    • Real-world validation achieved 98.25% accuracy via the mobile platform.

    Conclusions:

    • IDS offers an efficient, robust, and scalable framework for multi-disease anterior segment screening using smartphones.
    • Deployment via accessible mobile platforms can facilitate early detection and triage.
    • The system has the potential to enhance ophthalmic care access between specialized and primary care settings.