Accuracy of portable artificial intelligence-supported fundus camera in the screening of diabetic retinopathy in primary care.

OBJECTIVE

Research on the use of portable fundus cameras utilizing artificial intelligence (AI) for diabetic retinopathy (DR) screening in primary care remains limited. We aimed to evaluate the accuracy and reliability of DR screening in primary care using a smartphone-based, AI-assisted device in a small municipality in southern Brazil.

MATERIALS AND

METHODS

The reference standard was classification of fundus images by a retina specialist. Patients with diabetes enrolled in the Brazilian Family Health Program were recruited for the study. A general ophthalmologist obtained fundus images from 134 patients, and a retina specialist validated the DR diagnosis by AI.

RESULTS

The sample was predominantly female, with most patients having type 2 diabetes mellitus (T2DM). The age ranged from 17 to 81 years. Blood pressure was controlled in 34.9% of the sample. HbA1c levels ranged from 5.4% to 13.9%, and 35.3% of participants had levels below 7.0%. After excluding eight participants due to low image quality, the DR prevalence was 24.6%. The AI-based screening test for DR in primary care demonstrated a sensitivity of 100% (95% CI 88.8-100) and a specificity of 66.3% (95% CI 55.9-75.7). The negative predictive value (NPV) was 100% (95% CI 94.3-100), and the positive predictive value (PPV) was 49.2% (95% CI 36.4-62.1).

CONCLUSION

The smartphone-based, AI-assisted device showed good accuracy and excellent performance for DR screening in primary care. It can avoid unnecessary medical referrals and help prioritize patients with advanced disease who require early treatment to prevent severe complications.