Researchers in China have developed a deep-learning-based algorithm that could help radiologists detect potentially life-threatening cerebral aneurysms on CT angiography images. Cerebral aneurysms are weak spots in blood vessels in the brain, which can balloon out and fill with blood. If such a bulging aneurysm leaks or ruptures, it can cause serious symptoms and sometimes be fatal. The risk of rupture depends on the size, shape and location of the aneurysm, making detection and characterization of cerebral aneurysms vital. CT angiography, which uses X-ray CT to visualize blood vessels following injection of contrast into the bloodstream, is usually the first-line imaging exam for detecting cerebral aneurysms.

The aneurysm was missed in the initial report but successfully detected with the deep-learning algorithm. Researchers in China have developed a deep-learning-based algorithm that could help radiologists detect potentially life-threatening cerebral aneurysms on CT angiography images. Cerebral aneurysms are weak spots in blood vessels in the brain, which can balloon out and fill with blood. If such a bulging aneurysm leaks or ruptures, it can cause serious symptoms and sometimes be fatal. The risk of rupture depends on the size, shape and location of the aneurysm, making detection and characterization of cerebral aneurysms vital.

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).