人工智能驱动的基于影像的预测模型用于急性缺血性卒中机械取栓后出血转化：一项系统评价和荟萃分析。
完整摘要 · 中文译文
目的：出血转化（HT）是急性缺血性卒中（AIS）患者机械取栓（MT）后的主要并发症。尽管人工智能（AI）驱动的基于影像的预测模型在预测HT方面显示出前景，但其总体预测性能仍不确定。本系统综述和荟萃分析评估了其预测性能。 方法：检索PubMed、Embase、Cochrane Library、Web of Science、CNKI、万方、维普和CBM/SinoMed，检索时间自建库至2026年7月2日。纳入开发或验证AI驱动的基于影像的预测模型用于预测AIS患者MT后HT的研究，包括术前和术后影像预测因素。采用随机效应模型合并曲线下面积（AUC），采用双变量随机效应模型联合估计合并灵敏度和特异度并构建SROC曲线。亚组分析探讨了潜在的异质性来源。 结果：共纳入11项研究（总N = 3,541），其中10项研究提供了符合条件的AUC估计值，评估队列规模为18至261名参与者。合并AUC为0.891（95% CI：0.853-0.928），研究间异质性低（I 2 = 29.0%，p = 0.238）。双变量随机效应模型得出合并灵敏度为0.666（95% CI：0.578-0.744），合并特异度为0.919（95% CI：0.842-0.960）。探索性亚组分析未提供支持任何特定亚组优越性的稳健证据，其解释受到亚组样本量小和方法学的限制 异质性。然而，由于各亚组内研究数量有限，这些发现应谨慎解读。 结论：人工智能驱动的基于影像的预测模型在预测急性缺血性卒中（AIS）患者机械取栓（MT）后出血转化（HT）方面总体显示出良好的区分能力。然而，现有证据尚未确立任何特定模型类型的优越性。回顾性研究占主导、外部验证有限以及方法学异质性限制了当前发现的普适性。在临床实施之前，需要开展大型、多中心、具有严格外部验证的前瞻性研究。 系统综述注册：该系统综述已在PROSPERO注册（CRD420261441463）。
完整摘要 · 英文原文
OBJECTIVE: 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. METHODS: 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. RESULTS: Eleven studies (total N = 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 2 = 29.0%, p = 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. CONCLUSION: 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. SYSTEMATIC REVIEW REGISTRATION: The systematic review was registered with PROSPERO (CRD420261441463).