Real-world artificial intelligence-based interpretation of fundus imaging as part of an eyewear prescription renewal protocol

F-P Roubelat; V Soler; F Varenne; V Gualino

doi:10.1016/j.jfo.2024.104130

Real-world artificial intelligence-based interpretation of fundus imaging as part of an eyewear prescription renewal protocol

J Fr Ophtalmol. 2024 Mar 8:104130. doi: 10.1016/j.jfo.2024.104130. Online ahead of print.

Authors

F-P Roubelat¹, V Soler¹, F Varenne¹, V Gualino²

Affiliations

¹ Ophthalmology Department, Pierre-Paul Riquet Hospital, Toulouse University Hospital, Toulouse, France.
² Ophthalmology Department, Clinique Honoré-Cave, Montauban, France. Electronic address: dr.vgualino@gmail.com.

PMID: 38461084
DOI: 10.1016/j.jfo.2024.104130

Abstract

Objective: A real-world evaluation of the diagnostic accuracy of the Opthai® software for artificial intelligence-based detection of fundus image abnormalities in the context of the French eyewear prescription renewal protocol (RNO).

Methods: A single-center, retrospective review of the sensitivity and specificity of the software in detecting fundus abnormalities among consecutive patients seen in our ophthalmology center in the context of the RNO protocol from July 28 through October 22, 2021. We compared abnormalities detected by the software operated by ophthalmic technicians (index test) to diagnoses confirmed by the ophthalmologist following additional examinations and/or consultation (reference test).

Results: The study included 2056 eyes/fundus images of 1028 patients aged 6-50years. The software detected fundus abnormalities in 149 (7.2%) eyes or 107 (10.4%) patients. After examining the same fundus images, the ophthalmologist detected abnormalities in 35 (1.7%) eyes or 20 (1.9%) patients. The ophthalmologist did not detect abnormalities in fundus images deemed normal by the software. The most frequent diagnoses made by the ophthalmologist were glaucoma suspect (0.5% of eyes), peripapillary atrophy (0.44% of eyes), and drusen (0.39% of eyes). The software showed an overall sensitivity of 100% (95% CI 0.879-1.00) and an overall specificity of 94.4% (95% CI 0.933-0.953). The majority of false-positive software detections (5.6%) were glaucoma suspect, with the differential diagnosis of large physiological optic cups. Immediate OCT imaging by the technician allowed diagnosis by the ophthalmologist without separate consultation for 43/53 (81%) patients.

Conclusion: Ophthalmic technicians can use this software for highly-sensitive screening for fundus abnormalities that require evaluation by an ophthalmologist.

Keywords: Anomalies du fond d’œil; Artificial intelligence; Eyewear prescription renewal; Fundus abnormalities; Intelligence artificielle; Protocole de renouvellement optique; Sensibilité; Sensitivity; Specificity; Spécificité.