Fig. 7: Intelligent action decision and interpretable biometric measurement with deep neural networks.

a Comparison of performance between deep learning and non-deep learning method (k-nearest neighbors) in scanning action decision, as well as between instant and delayed decision-making. b Performance trends in action decision, biometric measurement, and plaque segmentation as training data scales up. c Comparison of the ROC curves for the models of stage transition, action decision, and their combined decision-making in accurately identifying the anatomical structures at the termination positions of each stage. d Performance of the model that predicts whether visible arterial wall and intimal structures exist in the longitudinal section images. e Precision-Recall curve of the model in detecting the local region that can be used to measure the arterial structural parameters. f Our interpretable biometric measurement solution compared with two other non-interpretable baseline models. The t-test was used to check whether the mean error of our method’s results is significantly smaller than that of Baseline 2. The models were evaluated on 1076 annotated images. This boxplot displays standard elements: the box represents the interquartile range (IQR), the central line marks the median, and the whiskers extend to 1.5 × IQR.