Deep Learning for MR Angiography: Automated Detection of Cerebral Aneurysms

To develop and evaluate a supportive algorithm using deep learning for detecting cerebral aneurysms at time-of-flight MR angiography to provide a second assessment of images already interpreted by radiologists. MR images reported by radiologists to contain aneurysms were extracted from four institutions for the period from November 2006 through October 2017. The images were divided into three data sets: training data set, internal test data set, and external test data set. The algorithm was constructed by deep learning with the training data set, and its sensitivity to detect aneurysms in the test data sets was evaluated. To find aneurysms that had been overlooked in the initial reports, two radiologists independently performed a blinded interpretation of aneurysm candidates detected by the algorithm. When there was disagreement, the final diagnosis was made in consensus. The number of newly detected aneurysms was also evaluated. The training data set, which provided training and validation data, included 748 aneurysms (mean size, 3.1 mm 2.0 [standard deviation]) from 683 examinations; 318 of these examinations were on male patients (mean age, 63 years 13) and 365 were on female patients (mean age, 64 years 13).