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Real-world integration of an autonomous artificial intelligence system for diabetic retinopathy screening in an
Or Gil1, Amiram Arad1, Miri Fogel1
1The Goldschleger Eye Institute, Gray Faculty of Medical & Health Sciences, Tel-Aviv University, Chaim Sheba Medical Center, Tel Aviv-Yafo, Tel Aviv District, Israel.
Aim:
To evaluate a real-world clinical integration of an autonomous artificial intelligence (AI) system (AEYE Diagnostic Screening (AEYE-DS), AEYE Health, USA) for diabetic retinopathy (DR) screening using the Topcon NW500 camera (Topcon, Japan) in an endocrinology clinic.
Methods:
Adults with type 1 or type 2 diabetes without previously reported DR attending routine endocrinology follow-up were invited to participate. Non-mydriatic, macula-centred fundus photographs were acquired by a novice, non-ophthalmic operator. Images were analysed by AEYE-DS to detect more-than-mild DR (mtmDR). AI-positive results prompted physician counselling and automated referral for internal confirmatory examination.
Results:
Definitive AEYE-DS results were obtained for 95.7% (245/256) of participants without pharmacological dilation. Seventy-six (29.6%) patients screened positive for mtmDR, of whom 34 (44.7%) completed confirmatory examination at the institution's retina clinic; externally completed follow-up was not captured. Four patients (11.8% of those evaluated) required treatment with intravitreal anti-vascular endothelial growth factor therapy or panretinal photocoagulation. Additional previously unrecognised ocular conditions were identified among several AI-positive patients. Patient satisfaction was high, with >80% reporting the screening was easy to use, time-efficient and recommendable.
Conclusions:
In a real-world endocrinology clinic, autonomous AI screening for DR using AEYE-DS integrated with the Topcon NW500 enabled efficient DR screening and achieved high non-mydriatic imageability. A clinically relevant proportion of patients requiring ophthalmic evaluation has been captured. Internal referral for confirmatory testing enabled assessment of downstream outcomes. The findings support scalable, point-of-care autonomous screening and a stepped-referral approach for AI-positive patients.
