Retina (Philadelphia, Pa.) · 2025
AUTOMATED MACHINE LEARNING CLASSIFICATION OF OPTICAL COHERENCE TOMOGRAPHY IMAGES OF RETINAL CONDITIONS USING GOOGLE CLOUD VERTEX ARTIFICIAL INTELLIGENCE.
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Sina Elliott M, Pena Jose, Zafar Sidra, Bommakanti Nikhil K, Kuriyan Ajay E, Yonekawa Yoshihiro
Аннотация
PURPOSE: 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.
METHODS: 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.
RESULTS: 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).
CONCLUSION: 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.
METHODS: 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.
RESULTS: 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).
CONCLUSION: 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.