Prospective evaluation of a deep learning algorithm deployed in an urban imaging centre to notify clinicians of head CT scans with critical abnormalities

Non-contrast Head-CT scans are primary imaging modality for evaluating patients with trauma or stroke. While results of deep learning algorithms to identify head-CT scans containing critical abnormalities has been published in retrospective studies, effects of deployment of such an algorithm in a real-world setting, with mobile notifications to clinician remain unstudied. In this prospective study, we evaluated performance of such an automated triage system in an urban 24-hour imaging facility.

We developed an accurate deep neural network algorithm that identifies and localizes intracranial bleeds, cranial fractures, mass effect and midline shift on non-contrast head-CT scans. The algorithm is deployed in clinical imaging facility in conjunction with an on-premise module that automatically selects eligible scans from PACS and uploads them to cloud-based algorithm for processing. Once processed, cloud algorithm returns an additional series, viewable as an overlay over the original, and a text notification to radiologist with preview images. Mobile notifications facilitated confirmation of the detected abnormalities. We studied the performance of the automated system over 60 days.

748 CT scans were taken over 60 days, of which 194 were non-contrast head-CT scans and these are evaluated by senior radiologist. Sensitivity, specificity, AUC and average time to notification of head-CT scans with critical abnormalities were  0.90 (95% CI 0.74-0.98), 0.86 (0.80-0.91), 0.97 (0.92-1.00) and 3.2 minutes respectively.

An automated triage system in a radiology facility results in rapid notification of critical scans, with a low false positive rate and this may be used to expedite treatment initiation.