Code-free automated machine learning for OCT-based classification of vitreoretinal interface diseases

BACKGROUND: Differentiation of vitreoretinal interface disorders on optical coherence tomography (OCT) relies on expert interpretation and can be challenging in borderline cases. Automated machine learning (AutoML) platforms may enable clinician-driven artificial intelligence development without coding expertise. This study evaluated the performance of a code-free AutoML approach for OCT-based classification. METHODS: In this cross-sectional image classification study, 434 OCT B-scans from publicly available datasets were manually labeled into four categories: epiretinal membrane (ERM), lamellar macular hole (LMH), full-thickness macular hole (MH), and normal retina. Images were uploaded to a cloud-based AutoML platform (Google Cloud Vertex AI), which automatically performed data splitting (80% training, 10% validation, 10% test), model training, and optimization. Performance was assessed using precision, recall, average precision, and confusion matrix analysis. RESULTS: The model achieved an overall average precision of 0.988, with precision and recall of 97.6%. MH and normal retina were classified with perfect precision and recall (100%). ERM showed high precision (100%) with slightly reduced recall (92.9%), while LMH demonstrated complete recall (100%) with lower precision (83.3%). Misclassifications were limited to anatomically related entities. CONCLUSIONS: Code-free AutoML enables accurate OCT-based classification of vitreoretinal interface disorders using a clinician-driven workflow. This approach may facilitate broader adoption of artificial intelligence in ophthalmology and support rapid clinical research prototyping.