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Subjective Refraction Test Using a Smartphone for Vision Screening
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Published on: October 18, 2024

Smartphone-based offline AI for multi-disease retinal screening: Real-world accuracy.

Aditya Kelkar1, Jai Kelkar1, Yash Garg1

  • 1Department of Vitreoretina Services, National Institute of Ophthalmology, Pune, India.

European Journal of Ophthalmology
|June 24, 2026
PubMed
Summary

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This summary is machine-generated.

An artificial intelligence system (MAI) showed high accuracy in diagnosing diabetic retinopathy, glaucoma, and AMD using a smartphone fundus camera. This technology supports scalable, point-of-care retinal screening, especially in underserved areas.

Area of Science:

  • Ophthalmology
  • Artificial Intelligence
  • Medical Diagnostics

Background:

  • Diabetic retinopathy (DR), glaucoma, and age-related macular degeneration (AMD) are leading causes of vision loss.
  • Early detection and screening are crucial for managing these conditions.
  • Smartphone-based fundus cameras offer potential for accessible eye care.

Purpose of the Study:

  • To assess the diagnostic accuracy of an offline artificial intelligence (AI) system, Medios-AI (MAI), for simultaneous screening of DR, glaucoma, and AMD.
  • To evaluate MAI's performance in a real-world setting using a smartphone-based fundus camera.
  • To compare MAI's diagnostic capabilities against expert ophthalmologists.

Main Methods:

  • A prospective cross-sectional study enrolled 193 adults (371 eyes).
Keywords:
Age-Related macular degeneration < RETINAdiabetic retinopathy < RETINAdiagnostic techniques < GLAUCOMApreventive medicine/Screening < SOCIOECONOMICS AND EDUCATION IN MEDICINE/OPHTHALMOLOGYtechniques of retinal examination < RETINAtelemedicine < SOCIOECONOMICS AND EDUCATION IN MEDICINE/OPHTHALMOLOGY

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Last Updated: Jun 25, 2026

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Smartphone Fundus Photography
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Published on: July 6, 2017

  • Fundus images were captured using smartphone-based and desktop fundus cameras.
  • An offline MAI algorithm analyzed images, and results were compared to masked grading by two ophthalmologists.

    • Main Results:

    • MAI demonstrated high sensitivity and specificity for detecting any retinal disease (99.3% and 95.7%, respectively).
    • Specific disease detection showed high accuracy: Glaucoma (98.2% sensitivity, 99.0% specificity), AMD (88.9% sensitivity, 97.5% specificity), and DR (84.6% sensitivity, 99.0% specificity).
    • Excellent agreement was observed between AI and human graders for vertical cup-to-disc ratio.

    Conclusions:

    • The Medios-AI system exhibits significant diagnostic accuracy for DR, glaucoma, and AMD.
    • Its offline, smartphone-based platform supports scalable, point-of-care retinal screening.
    • MAI is a promising tool for improving eye care accessibility, particularly in resource-limited settings.