Age prediction from coronary angiography using a deep neural network: Age as a potential label to extract prognosis-related imaging features
Fig 1
From a total of 7,360 coronary artery angiography (CAG) videos in 716 patients, 572 patients (5923 videos) were randomly allocated to the development dataset. This dataset was further split into 457 patients (4,771 videos) for training and 115 patients (1,152 videos) for validation. The remaining 144 patients (1437 videos) were allocated to the test dataset. The model was trained solely on CAG videos from the training dataset. Hyper parameter tuning and selection of the best model within 10 epochs was performed using the validation dataset. The test dataset was used solely for testing the performance of the final model. There were no overlaps in patients between the three datasets.