Deep-learning–based non-contrast CT for detecting acute ischemic stroke: a systematic review and HSROC meta-analysis of patient-level diagnostic accuracy

Abstract Background Non-contrast CT (NCCT) is first-line imaging for suspected acute ischemic stroke (AIS) but has limited early sensitivity; deep learning (DL) may improve patient-level detection. Objectives To estimate the diagnostic accuracy of DL applied to NCCT for patient-level AIS detection and to examine prespecified sources of between-study heterogeneity. Methods We searched MEDLINE, Embase, and Web of Science (January 2010–May 2025). Eligible prospective or retrospective diagnostic studies evaluated DL on NCCT against an appropriate reference standard and reported (or allowed reconstruction of) patient-level 2 × 2 data. Two-gate case–control and lesion-only reports were excluded. Dual reviewers screened/extracted data; risk of bias was assessed with QUADAS-2, and AI-reporting against items adapted from STARD-AI/CLAIM/CONSORT-AI. Bivariate random-effects/HSROC models summarized sensitivity and specificity. Prespecified moderators were posterior-fossa inclusion, reference-standard robustness, and validation type. Sensitivity analyses included external-only cohorts, robust standards, posterior-fossa inclusion, and a “Direct AIS” construct subset. Results Of 1,899 records, 16 studies met inclusion; 13 contributed patient-level data to meta-analysis. Summary sensitivity was 0.91 (95% CI, 0.81–0.96) and specificity 0.90 (0.85–0.94). Sensitivity was lower for externally validated models than internally validated ones (0.82 [0.67–0.91] vs. 0.95 [0.89–0.98]) with similar specificity (0.88 [0.83–0.92] vs. 0.93 [0.82–0.97]). Findings were directionally robust across sensitivity analyses. QUADAS-2 frequently indicated concerns in patient selection and index-test domains; AI-reporting quality was mostly moderate, and explicit external validation remained uncommon. Conclusions DL applied to NCCT shows high accuracy for patient-level AIS detection. However, generalizability is the principal gap; broader external validation and guideline-concordant reporting are needed to support safe clinical adoption.