Purpose: To investigate the diagnostic accuracy, feasibility and end-user experiences of an artificial intelligence (AI)-based, automated diabetic retinopathy (DR) screening model in real-world, Australian primary care and endocrinology clinics.
Methods: In a pragmatic trial conducted across five sites including general practice and endocrinology clinics, from August 2021 to June 2023, patients aged ≥50 years, and those aged ≥18 years with diabetes were screened using an AI-integrated, non-mydriatic fundus camera. The AI instantly analysed the retinal images for referable DR. Patients detected with referable DR or ungradable images were referred to eyecare professionals. The accuracy of the AI grading was assessed against gold standard human grading. A satisfaction survey was administered among the participants and care providers.
Results: Among 863 participants enrolled (mean (SD) age: 62.6 (13.2) years; 53.0% women), the AI system achieved high accuracy of 93.3% (95% CI: 91.4% to 95.5%) for referable DR detection, with a sensitivity of 83.7% (95% CI: 78.2% to 88.3%), specificity of 96.1% (95% CI: 94.7% to 97.2%) and an area under the receiver operating characteristic curve of 0.899 (95% CI: 0.874 to 0.924). The proportion of ungradable images was lower according to the AI grading (13.4%) compared with human grading (15.6%). Most patients (86%) and care providers (85%) expressed high satisfaction with the AI system.
Conclusions: The AI-assisted DR screening model was accurate and well received by patients and staff in Australian primary care and endocrinology clinics. This opportunistic screening model holds promise for enhancing early DR detection in non-eyecare settings, potentially preventing vision loss due to DR on a considerable scale.
Keywords: Diagnostic tests/Investigation; Humans; Imaging; Public health; Retina.
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