AUTO MACHINE LEARNING FOR DIABETIC RETINOPATHY SCREENING: A HEAD-TO-HEAD MULTI-PLATFORM COMPARISON AGAINST HUMAN GRADERS AND IDX-DR
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The authors aimed to benchmark various automated machine learning (AutoML) platforms for diabetic retinopathy (DR) screening against human graders and the FDA-approved IDX-DR system. They found that Amazon SageMaker Canvas and AutoGluon exhibited the strongest performance, particularly in detecting sight-threatening DR, while highlighting the variability in effectiveness across different platforms and thresholds. The study underscores the potential of AutoML in clinical settings, emphasizing the need for external validation and calibration of decision thresholds.
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