Automated Detection of Ischemic Stroke and Subsequent Patient Triage in Routinely Acquired Head CT.

Purpose: Advanced machine-learning (ML) techniques can potentially detect the entire spectrum of pathology through deviations from a learned norm. We investigated the utility of a weakly supervised ML tool to detect characteristic findings related to ischemic stroke in head CT and provide subsequent patient triage.

Methods: Patients having undergone non-enhanced head CT at a tertiary care hospital in April 2020 with either no anomalies, subacute or chronic ischemia, lacunar infarcts of the deep white matter or hyperdense vessel signs were retrospectively analyzed.

Automated Pathology Detection and Patient Triage in Routinely Acquired Head Computed Tomography Scans.

Objectives: Anomaly detection systems can potentially uncover the entire spectrum of pathologies through deviations from a learned norm, meaningfully supporting the radiologist's workflow. We aim to report on the utility of a weakly supervised machine learning (ML) tool to detect pathologies in head computed tomography (CT) and adequately triage patients in an unselected patient cohort.

Materials And Methods: All patients having undergone a head CT at a tertiary care hospital in March 2020 were eligible for retrospective analysis.