Machine Learning-Enabled Detection of Unruptured Cerebral Aneurysms Improves Detection Rates and Clinical Care

Unruptured cerebral aneurysms (UCAs) have a relatively low prevalence of ≈3%, but detection can prevent devastating consequences of subarachnoid hemorrhage. Here, we assess the performance of a machine learning algorithm to identify UCAs and determine whether routine use of the algorithm improves detection and patient care.

From a prospectively maintained multicenter registry across 8 certified stroke centers (1 comprehensive and 7 primary), we identified patients who underwent computed tomography angiography for evaluation of possible stroke from March 14, 2021, to November 31, 2021. A convolutional deep neural network (Viz ANEURYSM) trained to identify UCAs at least 4 mm in size analyzed the images, and ground truth was provided by a blinded expert neuroradiologist. The primary outcome was rate of clinical follow-up for UCAs detected by the machine learning algorithm.

Among 1191 computed tomography angiograms performed during the study period, 50 (4.2%) were flagged by the machine learning algorithm as possibly demonstrating a UCA, of which 31 cases were confirmed as true positive (positive predictive value, 62%). There were a total of 36 true aneurysms with 4 cases of multiple aneurysms. Overall, the most common locations included internal carotid artery (42%). Of these cases, 10 (27.8%) were not noted in the clinical radiology report or clinical notes, with a median size of 4.4 mm (interquartile range, 1.6 mm), and 24 (67%) were not referred for follow-up, with median size of 4.4 mm (interquartile range, 4.2 mm). Of the 24 aneurysms not referred for follow-up, 15 (62.5%) had been noted in the radiology report. A total of 33.3% (5/15) of the detected but not referred cases had a diameter >7 mm, with median PHASES score of 7.

UCAs of sizes and intradural locations that require attention and may warrant treatment are frequently missed in routine clinical care. A machine learning algorithm that flags studies and notifies clinicians may minimize missed care opportunities.