SynthesisBMC neurology2025
Deep-learning-based non-contrast CT for detecting acute ischemic stroke: a systematic review and HSROC meta-analysis of patient-level diagnostic accuracy.
Synthesis in BMC neurology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
6 citing papers in PubMed.
- Machine learning in neuroradiology: Recent developments and applications.World journal of radiology · 2026Review
- AGTS-Net: Anatomically Guided Two-Stage Network for Actionable Stroke Classification in Complete NCCT Studies.Bioengineering (Basel, Switzerland) · 2026Article
- Early Diagnosis of Ischemic Stroke on Non-Contrast CT Scans Using a Convolutional Neural Network: A Case Study from Mulago National Referral Hospital, Uganda.Research square · 2026Article
- Machine learning versus deep learning for screening ischemic stroke among asymptomatic population.Scientific reports · 2026Article
- Artificial intelligence in neurology practice: promise, perils, and a roadmap for responsible integration.Journal of neurology · 2026Review
- The Role of Artificial Intelligence in Acute Ischemic Stroke Care: A Narrative Review.Missouri medicineReview
Corrections and comments
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Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundNon-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.
objectivesTo estimate the diagnostic accuracy of DL applied to NCCT for patient-level AIS detection and to examine prespecified sources of between-study heterogeneity.
methodsWe 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.
resultsOf 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.
conclusionsDL 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.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.