Fundus camera and artificial intelligence for diabetic retinopathy screening in a São Paulo shelter.
- 1Irmandade da Santa Casa de Misericórdia de São Paulo - São Paulo (SP), Brazil.
- 2Irmandade da Santa Casa de Misericórdia de São Paulo, Department of Ophthalmology - São Paulo (SP), Brazil.
Abstract
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.
Related
- Artificial intelligence-supported diabetic retinopathy screening in Tanzania: a randomised controlled trial
- Clinical setting-dependent diagnostic accuracy of artificial intelligence and store-and-forward diabetic retinopathy screening: a systematic review and meta-analysis
- Performance Assessment of an Artificial Intelligence System Used for Case Detection in a Large Community-based Diabetic Retinopathy Screening Program: A Retrospective Diagnostic Accuracy Validation Study.
- A Robust and Trustable Approach to Incorporate Medical Domain Knowledge in Machine Learning Models for Diabetic Retinopathy Screening Using Routine Lab Results
- ACCESS2 study: the potential for autonomous AI to mitigate disparities in diabetic retinopathy screening in youth
BibTeX
@article{dias2026fundus,
title = {{Fundus camera and artificial intelligence for diabetic retinopathy screening in a São Paulo shelter.}},
author = {Julia Gimenes da Silva Dias and Gabriel Costa de Andrade and João Victor Tenório Lossio de Macedo and Andrezza Soldera Graziano},
journal = {Revista da Associacao Medica Brasileira (1992)},
year = {2026},
month = oct,
doi = {10.1590/1806-9282.20260862},
url = {https://europepmc.org/article/MED/42912071},
note = {CC BY},
}
RIS
TY - JOUR
TI - Fundus camera and artificial intelligence for diabetic retinopathy screening in a São Paulo shelter.
AU - Julia Gimenes da Silva Dias
AU - Gabriel Costa de Andrade
AU - João Victor Tenório Lossio de Macedo
AU - Andrezza Soldera Graziano
PY - 2026
DA - 2026/10/16
JO - Revista da Associacao Medica Brasileira (1992)
DO - 10.1590/1806-9282.20260862
UR - https://europepmc.org/article/MED/42912071
AB - 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.
SN - 0104-4230
SN - 1806-9282
ER -