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Related Experiment Video

Updated: Aug 1, 2025

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Published on: November 30, 2022

Detecting visually significant cataract using retinal photograph-based deep learning.

Yih-Chung Tham^1,2,3, Jocelyn Hui Lin Goh¹, Ayesha Anees⁴

¹Singapore Eye Research Institute, Singapore National Eye Centre, Singapore, Singapore.

|April 28, 2023

Summary

This summary is machine-generated.

A new deep-learning algorithm can detect visually significant cataracts from retinal images, improving screening accessibility for older adults. This AI tool shows high accuracy, comparable to ophthalmologists, aiding early diagnosis and treatment.

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Area of Science:

Ophthalmology
Artificial Intelligence
Medical Imaging

Background:

Age-related cataracts are a primary cause of vision loss in the elderly.
Limited access to cataract screening leads to underdiagnosis and neglect in communities.

Purpose of the Study:

To develop and validate a deep-learning algorithm for automated detection of visually significant cataracts using retinal photographs.
To assess the algorithm's performance against human expert evaluations.

Main Methods:

Utilized over 25,000 retinal images from population-based studies for algorithm development and internal validation.
Performed external validation across three independent studies.
Compared algorithm performance (sensitivity, specificity) against four ophthalmologists in a separate test set.

Main Results:

Achieved an area under the receiver operating characteristic curve (AUROC) of 96.6% in the internal test set.
External testing demonstrated AUROCs ranging from 91.6% to 96.5%.
The algorithm exhibited comparable or superior performance to ophthalmologists in sensitivity (93.3% vs. 51.7-96.6%) and specificity (99.0% vs. 90.7-97.9%).

Conclusions:

A retinal photograph-based AI tool shows significant potential for screening visually significant cataracts in older adults.
This technology can improve access to screening and facilitate appropriate referrals to eye care centers.
Automated detection offers a scalable solution to address the unmet need for cataract screening in underserved populations.