Machine-learning algorithm in acute stroke: real-world experience. Issue 2 (February 2023)

• Record Type: Journal Article
• Authors: Chan, N.; Sibtain, N.; Booth, T.; de Souza, P.; Bibby, S.; Mah, Y.-H.; Teo, J.; U-King-Im, J.M.
• Is Part Of: Clinical radiology. Volume 78:Issue 2(2023)
• Page Start: e45
• Page End: e51
• Publication Date: 2023-02
• Subjects: Medical radiology -- Periodicals; Radiotherapy -- Periodicals; Radiotherapy -- Periodicals; Radiology -- Periodicals; Societies, Medical -- Periodicals; Medical radiology; Radiotherapy; Electronic journals; Periodicals; 616.0757
• DOI: 10.1016/j.crad.2022.10.007
• Languages: English
• ISSNs: 0009-9260

Abstract

Abstract : 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 patients (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 (RAPID™ 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). RAPID™ 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, RAPID™ ASPECTS showed good correlation with neuroradiologists' blinded independent interpretation, Pearson correlation coefficient = 0.96 (both readers), 0.63 (RAPID™ vs reader 1), 0.69 (RAPID™ vs reader 2). RAPID™ 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: RAPID™ CTA was robust and reliable in detection of LVO. Although demonstrating high sensitivity and NPV, RAPID™ ASPECTS interpretation was associated with a high number of false positives, which decreased clinicians' confidence in the algorithm. However, in cases of proven LVO, RAPID™ ASPECTS performed well and had good correlation with neuroradiologists' blinded interpretation. Highlights: RAPID TM ASPECTS: Demonstrated high sensitivity, but poor specificity for acute infarcts. Is associated with a high percentage of false positives. Correlates well with neuroradiologists' interpretation in cases of LVO. RAPID TM CTA demonstrated high sensitivity and specificity for LVO detection.