Automated machine learning (AutoML) is a novel artificial intelligence (AI) strategy that enables clinicians without coding experience to develop their own AI models. This study assessed the discriminative performance of AutoML in differentiating diabetic retinopathy (DR), central retinal vein occlusion (CRVO) and branch retinal vein occlusion (BRVO) from normal fundi using color fundus photographs (CFPs).

We carried out AutoML model design using CFPs retrieved from a publicly available CFP data set (3200 labelled images). The retrieved CFPs were reviewed for quality and then uploaded to the Google Cloud Vertex AI platform for AutoML training and testing. We trained a multi-class classification model to differentiate DR, CRVO, BRVO from normal fundi using 875 CFPs and externally validated the model using 210 CFPs obtained from another dataset. Performance metrics, including area under receiver operator curve (AUROC) and sensitivity were reported. We then compared the AutoML model to state-of-the-art deep learning (DL)-based DR and RVO multi-class models identified through a literature search.

Our AutoML model showed high discriminative performance in the multi-class classification of DR, CRVO and BRVO based on CFPs, with an AUROC, precision and recall reaching 0.995, 95.4% and 95.4% respectively at the 0.5 confidence threshold. The per-label sensitivity and specificity, respectively, were normal fundi (97.5%, 100%), DR (100%, 93.88%), CRVO (66.67%, 100%) and BRVO (71.43%, 98.73%). Our AutoML model generally showed similar performance to the state-of-the-art DL classifiers.

Our AutoML model can detect DR, CRVO, and BRVO in CFPs with good diagnostic accuracy and is a potentially useful screening tool.