1. Predicting Age From Optical Coherence Tomography Scans With Deep Learning

    Predicting Age From Optical Coherence Tomography Scans With Deep Learning

    Purpose : To assess whether age can be predicted from deep learning analysis of peripapillary spectral-domain optical coherence tomography (SD-OCT) B-scans and to determine the importance of specific retinal areas on the predictions. Methods : Deep learning (DL) convolutional neural networks were developed to predict chronological age in healthy subjects using peripapillary SD-OCT B-scan images. Models were built using the whole B-scan, as well as using specific regions through image ablation. Cross-validation was used for training and testing the model. Mean absolute error (MAE) and correlations between predicted and observed age were used to evaluate model performance. Results : A total of 7271 ...

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