Fundus camera and artificial intelligence for diabetic retinopathy screening in a São Paulo shelter.

OBJECTIVE

The aim of this study was to evaluate the applicability of a smartphone-based portable fundus camera for diabetic retinopathy screening in elderly residents of a specialized shelter in São Paulo, Brazil, considering image acquisition by non-ophthalmologist professionals and remote case classification.

METHODS

Prospective pilot interventional study at the Centro de Acolhida Especial para Idosos Morada São João. Thirty-eight participants (76 eyes) underwent retinal imaging using the Eyer® portable fundus camera (Phelcom Technologies, Brazil) by trained non-ophthalmologist professionals. Images were remotely evaluated by a retinal specialist and compared to the EyerMaps® artificial intelligence system.

RESULTS

Mean age was 71.6±7.0 years; 78.9% male. Fifteen participants (39.5%) had confirmed diabetes mellitus. The specialist identified abnormalities in 70 of 76 eyes (92.1%), most frequently glaucoma suspicion, media opacity suggestive of cataract, chorioretinal changes, and age-related macular degeneration signs; no diabetic retinopathy was identified. Against the specialist's assessment of any ocular abnormality as the reference standard, the artificial intelligence system classified 22 eyes as abnormal, yielding sensitivity 25.7% (95%CI 16.9-37.0), specificity 33.3% (95%CI 9.7-70.0), positive predictive value 81.8% (95%CI 61.5-92.7), negative predictive value 3.7% (95%CI 1.0-12.5), and overall accuracy 26.3% (95%CI 17.7-37.2).

CONCLUSION

The Eyer® device demonstrated operational feasibility for ocular screening by trained non-ophthalmologists. The high prevalence of media opacities and non-diabetic retinopathy conditions compromised artificial intelligence performance, highlighting the need for broader screening strategies in elderly and socially vulnerable populations.