Excellent agreement between automated deep learning-based and manual DWI infarct volume measurement in hyperacute stroke

Abstract

BackgroundDiffusion-weighted imaging (DWI) lesion volume and infarct growth are important imaging markers in acute ischemic stroke, but manual volume measurement is time-consuming and resource-intensive. Deep learning (DL)-based automated segmentation may facilitate rapid assessment; however, its performance on hyperacute DWI has not been sufficiently assessed. The aim was to evaluate the agreement between DL-based automated and manual infarct volume measurements and to compare their ability to predict clinical outcomes in patients treated with mechanical thrombectomy (MT). MethodConsecutive MT-treated patients (September 2014-December 2019) who underwent DWI at admission and at approximately 24 hours were retrospectively analyzed. Manual infarct volume was measured by stroke neurologists. Automated measurements were obtained using DL-based software. Agreement was assessed using Pearsons correlation, Bland-Altman analysis, and intraclass correlation coefficients (ICC 2,1). Inter- and intra-rater reliabilities were evaluated in a randomly selected subgroup of 150 patients. Predictive ability for a good outcome at 3 months (modified Rankin Scale score 0-2 or stable/improved from premorbid status) was compared using C-statistics and DeLongs test. ResultsA total of 371 patients (677 DWI scans) were included. Manual and automated measurements showed very strong correlation (r = 0.96) with minimal bias (-1.77 mL). The ICC for manual-automated agreement was 0.959 (95% CI, 0.952-0.965), comparable to inter- and intra-rater ICCs. Agreement remained high across onset-to-imaging times and lesion sizes. Predictive abilities for a good outcome were similar for manual and automated admission DWI volume (C-statistics 0.867 vs. 0.861) and infarct growth (0.859 vs. 0.853). Manual follow-up DWI volume showed slightly better predictive ability than automated measurement (0.880 vs. 0.866). ConclusionDL-based automated infarct volume measurement shows excellent agreement with experienced clinicians, with predictive performance comparable to manual assessment. Automated DWI-based quantification is reliable and feasible for use in hyperacute stroke management.