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Health·G Space·Evidence-backed gain·Published 2026-07-27

AI-based clinician decision support system for diagnosis of inherited retinal diseases: a multicenter, randomized trial

The accurate and timely diagnosis of inherited retinal diseases (IRDs) represents an unmet clinical need in ophthalmology, as the current pathways rely on resource-intensive phenotyping, multidisciplinary expertise and genetic testing. Here we developed Retina4IRD, an artificial intelligence (AI)-based clinician decision support system (CDSS) that predicts 17 genotype categories from retina images. Retina4IRD uses a Vision Transformer model pretrained with RETFound. We then trained and validated Retina4IRD using…

TRV-2026-0575Peer-reviewedPermanent record — cite & verify
AI-based clinician decision support system for diagnosis of inherited retinal diseases: a multicenter, randomized trial

"CRE-003 OphaGo - Retinal Imaging Adapter - Huced Despro ITS" by Djoko Kuswanto is marked with CC0 1.0. To view the terms, visit https://creativecommons.org/publicdomain/zero/1.0/.

The quick read

Researchers developed Retina4IRD, an AI-based clinician decision support system that predicts genotype categories from fundus photographs and OCT scans, and tested it in internal and external validation and in a 300-participant randomized controlled trial comparing AI-assisted specialists to specialists alone for suspected inherited retinal diseases.

The trial matters because inherited retinal disease diagnosis currently relies on resource-intensive phenotyping and genetic testing; the results show AI assistance prior to genetic testing can increase diagnostic accuracy and improve management scores, though generalizability beyond the studied populations and long-term clinical outcomes remain to be established.

Main points
  • Retina4IRD is a Vision Transformer model pretrained with RETFound that predicts 17 genotype categories from retina images.
  • Model was trained and validated on multimodal data from 1,843 genetically confirmed patients (3,376 eyes) across China, South Korea and Poland.
  • Randomized trial enrolled 300 participants with suspected IRD, with 295 included in final analysis after next-generation sequencing, median age 33 years.
  • Secondary endpoints favored AI-assisted arm including top-1 accuracy 37.8% versus 22.4% and top-4 accuracy 81.8% versus 53.1%.
Gain

Clinicians assisted by Retina4IRD achieved higher top-5 genetic diagnosis accuracy and better downstream management scores for suspected inherited retinal disease compared to specialist-only care.

The rundown

Retina4IRD was developed to predict 17 genotype categories from color fundus photographs and optical coherence tomography scans, using a Vision Transformer pretrained with RETFound and trained on 1,843 genetically confirmed patients.

In the randomized controlled trial, 300 participants with suspected IRD were randomized 1:1 to Retina4IRD-assisted specialist versus specialist-only, with final analysis on 295 participants with available next-generation sequencing reports.

Beyond top-5 accuracy, top-1 to top-4 accuracies all favored the assisted arm, and post hoc analyses showed better management decisions with a composite downstream management score of 37.7 versus 28.5.

Sources

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