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A generalised computer vision model for improved glaucoma screening using fundus images

Medicine and Health

A generalised computer vision model for improved glaucoma screening using fundus images

A. K. Chaurasia, G. Liu, et al.

Discover a groundbreaking study where a team of researchers, including Abadh K. Chaurasia and Puya Gharahkhani, have developed a high-performing deep-learning algorithm for glaucoma screening using diverse fundus images. With impressive metrics demonstrated on extensive datasets, this research paves the way for future advancements in population-level healthcare.

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Playback language: English
Abstract
Glaucoma, a leading cause of blindness, necessitates timely detection. This study develops and validates a generalized deep-learning algorithm for glaucoma screening using fundus images from 20 diverse global databases (18,468 images). The best-performing model (vgg19_bn) achieved high AUROC (0.9920), sensitivity, specificity, accuracy, precision, and F1-score (>0.9530) on the training dataset. External validation on the Drishti-GS dataset showed an AUROC of 0.8751 and accuracy of 0.8713. While accuracy decreased slightly on unseen data, highlighting dataset inconsistencies, the model demonstrates potential for population-level glaucoma screening, though further refinement with larger, diverse datasets is needed.
Publisher
Eye
Published On
Nov 05, 2024
Authors
Abadh K. Chaurasia, Guei-Sheung Liu, Connor J. Greatbatch, Puya Gharahkhani, Jamie E. Craig, David A. Mackey, Stuart MacGregor, Alex W. Hewitt
Tags
glaucoma
deep-learning
screening
fundus images
AUROC
healthcare
disease detection

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