AI in the Clinical Management of GA: A Novel Therapeutic Universe Requires Novel Tools

AI Generated Summary*

A narrative review argues that OCT analyzed by artificial intelligence suits geographic atrophy care better than fundus autofluorescence, because photoreceptor and other subclinical changes escape human grading. It surveys deep learning segmentation of atrophy, ellipsoid zone loss, drusenoid deposits and hyperreflective foci, plus models forecasting conversion and lesion growth. Post hoc trial analyses of pegcetacoplan showed ellipsoid zone loss growth cut by 46 to 53 percent at 24 months, against 20 to 27 percent for RPE loss. Larger baseline ellipsoid zone loss beyond the RPE defect predicted faster growth and stronger response. The review also covers regulation, device variability, microperimetry, FDA acceptance of ellipsoid zone attenuation as an endpoint, and screening.

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