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TRUVACE RECORD VERSION
record: TRV-2026-0575
version: 1
kind: certified
reason: Certified into the record
timestamp: 2026-07-27T06:07:56.063111Z
status: published
lens: g_space
sector: health
headline: AI-based clinician decision support system for diagnosis of inherited retinal diseases: a multicenter, randomized trial
dek: 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…
gain_title: 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.
problem_title: (none)
trace_subject: (none)
gain_reading: 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.
gain_evidence: top-5 genetic accuracy was significantly higher in the Retina4IRD-assisted specialist arm versus the specialist-only arm (88.5% versus 67.3%, P < 0.001) | composite downstream management score indicated significantly higher scores relative to the control group (37.7 versus 28.5, P < 0.001)
problem_reading: (none)
problem_evidence: (none)
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.
limitation: 
tag: Evidence-backed gain
key_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%.
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:
- peer_reviewed | Nature Medicine | https://doi.org/10.1038/s41591-026-04545-w | 2026-07-24
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