Artificial intelligence-driven imaging-based prediction models for hemorrhagic transformation after mechanical thrombectomy in acute ischemic stroke: a systematic review and meta-analysis.
Authors
Affiliations (1)
Affiliations (1)
- Beijing Luhe Hospital, Capital Medical University, Beijing, China.
Abstract
Hemorrhagic transformation (HT) is a major complication after mechanical thrombectomy (MT) in acute ischemic stroke (AIS). Although artificial intelligence (AI)-driven imaging-based prediction models have shown promise for predicting HT, their overall predictive performance remains uncertain. This systematic review and meta-analysis evaluated their predictive performance. PubMed, Embase, Cochrane Library, Web of Science, CNKI, Wanfang, VIP, and CBM/SinoMed were searched from inception to July 2, 2026. Studies developing or validating AI-driven imaging-based prediction models for predicting HT after MT in AIS, including both pre- and post-procedural imaging predictors, were included. A random-effects model was used to pool the area under the curve (AUC), while a bivariate random-effects model was used to jointly estimate pooled sensitivity and specificity and construct the SROC curve. Subgroup analyses explored potential sources of heterogeneity. Eleven studies (total <i>N</i> = 3,541) were included, 10 studies contributed eligible AUC estimates, with evaluation cohorts ranging from 18 to 261 participants. The pooled AUC was 0.891 (95% CI: 0.853-0.928), with low between-study heterogeneity (<i>I</i> <sup>2</sup> = 29.0%, <i>p</i> = 0.238). The bivariate random-effects model yielded a pooled sensitivity of 0.666 (95% CI: 0.578-0.744) and a pooled specificity of 0.919 (95% CI: 0.842-0.960). Exploratory subgroup analyses did not provide robust evidence supporting the superiority of any specific subgroup, and their interpretation was limited by small subgroup sizes and methodological heterogeneity. However, these findings should be interpreted cautiously because of the limited number of studies within individual subgroups. AI-driven imaging-based prediction models showed generally favorable discrimination in predicting HT after MT in AIS. However, current evidence does not establish the superiority of any specific model type. The predominance of retrospective studies, limited external validation, and methodological heterogeneity limit the generalizability of current findings. Large, multicenter prospective studies with rigorous external validation are needed before clinical implementation. The systematic review was registered with PROSPERO (CRD420261441463).