Machine-learning algorithm in acute stroke: real-world experience

AIM: To assess the clinical performance of a commercially available machine learning (ML) algorithm in acute stroke.MATERIALS AND METHODS: CT and CT angiography (CTA) studies of 104 consecutive pa-tients (43 females, age range 19-93, median age 62) performed for suspected acute stroke at a single tertiary institution with real-time ML software analysis (RAPIDTM ASPECTS and CTA) were included. Studies were retrospectively reviewed independently by two neuroradiologists in a blinded manner. RESULTS: The cohort included 24 acute infarcts and 16 large vessel occlusions (LVO). RAPIDTM ASPECTS interpretation demonstrated high sensitivity (87.5%) and NPV (87.5%) but very poor specificity (30.9%) and PPV (30.9%) for detection of acute ischaemic parenchymal changes. There was a high percentage of false positives (51.1%). In cases of proven LVO, RAPIDTM ASPECTS showed good correlation with neuroradiologists' blinded independent interpretation, Pearson correlation coefficient = 0.96 (both readers), 0.63 (RAPIDTM vs reader 1), 0.69 (RAPIDTM vs reader 2).RAPIDTM CTA interpretation demonstrated high sensitivity (92.3%), specificity (85.3%), and negative predictive (NPV) (98.5%) with moderate positive predictive value (PPV) (52.2%) for detection of LVO (N=13). False positives accounted for 12.5% of cases, of which 27.3% were attributed to arterial stenosis.CONCLUSION: RAPIDTM CTA was robust and reliable in detection of LVO. Although demon-strating high sensitivity and NPV, RAPIDTM ASPECTS interpretation was associated with a high number of false positives, which decreased clinicians' confidence in the algorithm. However, in cases of proven LVO, RAPIDTM ASPECTS performed well and had good correlation with neu-roradiologists' blinded interpretation. (c) 2022 The Royal College of Radiologists. Published by Elsevier Ltd. All rights reserved.