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-

SUBMITTER: Gete KY 

PROVIDER: S-EPMC12706894 | biostudies-literature | 2025 Dec

REPOSITORIES: biostudies-literature