AUTOMATED MACHINE LEARNING CLASSIFICATION OF OPTICAL COHERENCE TOMOGRAPHY IMAGES OF RETINAL CONDITIONS USING GOOGLE CLOUD VERTEX ARTIFICIAL INTELLIGENCE.

Abstract

Automated machine learning is an artificial intelligence tool that streamlines image recognition model development. This study evaluates the diagnostic performance of Google Vertex AI automated machine learning in differentiating age-related macular degeneration (AMD), diabetic macular edema, epiretinal membrane, retinal vein occlusion, and healthy controls using optical coherence tomography images. A publicly available, validated optical coherence tomography data set of 1965 deidentified images from 759 patients was used. Images were labeled and uploaded to Vertex AI. A single-label classification model was trained, validated, and tested using an 80%-10%-10% split. Diagnostic metrics included area under the precision-recall curve (AUPRC), sensitivity, specificity, and positive and negative predictive value. A subanalysis evaluated neovascular versus nonneovascular AMD. The automated machine learning model achieved high accuracy (AUPRC = 0.991), with sensitivity, specificity, and PPV of 95.9%, 96.9%, and 95.9%, respectively. AMD classification performed best (AUPRC = 0.999, precision = 98.4%, recall = 99.2%). Epiretinal membrane (AUPRC = 0.978, precision = 92.9%, recall = 86.7%) and diabetic macular edema (AUPRC = 0.895, precision = 81.3%, recall = 86.7%) followed. Retinal vein occlusion recall was 80% despite 100% precision. Neovascular AMD outperformed nonneovascular AMD (AUPRC = 0.963 vs. 0.915). Our automated machine learning model accurately classifies optical coherence tomography images of retinal conditions, demonstrating performance comparable or superior to traditional ML methods. Its user-friendly design supports scalable AI-driven clinical integration.