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Mi-S1.1-IAIM1: Inteligencia artificial en imagen médica (I) Lugar: Salón de Actos Presidente de la sesión: Andrés Santos Lleó Presidente de la sesión: Julia Ramírez García | |
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12:00 - 12:15
Deep Learning-based age prediction models from retinal Optical Coherence Tomography images 1: Mondragon Goi Eskola Politeknikoa, España; 2: Instituto de Investigación Sanitaria Biobizkaia; 3: Ikerbasque, Fundación Vasca para la Ciencia This study evaluates the potential of Optical Coherence Tomography (OCT) as a non-invasive tool for retinal age prediction in healthy individuals. A dataset comprising 1,180 eyes from 517 control subjects was used to compare deep learning models trained on different OCT scan types: peripapillary B-scans, individual macula raster B-Scans, and full macular volumes. Images underwent standardized preprocessing, and models based on 2D and 3D ResNet architectures were trained and optimized using Transfer Learning. Results show that volumetric macular scans applied in a ResNet-3D model achieved the lowest Mean Absolute Error (3.07 years), outperforming both previous literature and all tested 2D configurations. Overall, findings highlight that integrating depth and spatial features in OCT data significantly enhances retinal age estimation.
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