Wang Xinrui, Fan Yiming, Zhang Nan, Li Jing, Duan Yang, Yang Benqiang
Department of Radiology, General Hospital of Northern Theater Command, Shenyang, China.
Department of Orthopedics, Chinese PLA General Hospital, Beijing, China.
Front Neurol. 2022 Jul 8;13:910259. doi: 10.3389/fneur.2022.910259. eCollection 2022.
Machine learning (ML) has been proposed for lesion segmentation in acute ischemic stroke (AIS). This study aimed to provide a systematic review and meta-analysis of the overall performance of current ML algorithms for final infarct prediction from baseline imaging. We made a comprehensive literature search on eligible studies developing ML models for core infarcted tissue estimation on admission CT or MRI in AIS patients. Eleven studies meeting the inclusion criteria were included in the quantitative analysis. Study characteristics, model methodology, and predictive performance of the included studies were extracted. A meta-analysis was conducted on the dice similarity coefficient (DSC) score by using a random-effects model to assess the overall predictive performance. Study heterogeneity was assessed by Cochrane and Higgins tests. The pooled DSC score of the included ML models was 0.50 (95% CI 0.39-0.61), with high heterogeneity observed across studies ( 96.5%, < 0.001). Sensitivity analyses using the one-study removed method showed the adjusted overall DSC score ranged from 0.47 to 0.52. Subgroup analyses indicated that the DL-based models outperformed the conventional ML classifiers with the best performance observed in DL algorithms combined with CT data. Despite the presence of heterogeneity, current ML-based approaches for final infarct prediction showed moderate but promising performance. Before well integrated into clinical stroke workflow, future investigations are suggested to train ML models on large-scale, multi-vendor data, validate on external cohorts and adopt formalized reporting standards for improving model accuracy and robustness.
机器学习（ML）已被应用于急性缺血性卒中（AIS）的病灶分割。本研究旨在对当前用于从基线影像预测最终梗死灶的ML算法的整体性能进行系统评价和荟萃分析。我们对符合条件的研究进行了全面的文献检索，这些研究开发了用于估计AIS患者入院时CT或MRI上核心梗死组织的ML模型。定量分析纳入了11项符合纳入标准的研究。提取了纳入研究的特征、模型方法和预测性能。采用随机效应模型对骰子相似系数（DSC）评分进行荟萃分析，以评估整体预测性能。通过Cochrane检验和Higgins检验评估研究异质性。纳入的ML模型的合并DSC评分为0.50（95%CI 0.39-0.61），各研究间观察到高度异质性（I²=96.5%，P<0.001）。使用逐一剔除研究的方法进行的敏感性分析显示，调整后的整体DSC评分范围为0.47至0.52。亚组分析表明，基于深度学习（DL）的模型优于传统ML分类器，在结合CT数据的DL算法中观察到最佳性能。尽管存在异质性，但当前基于ML的最终梗死灶预测方法表现出中等但有前景的性能。在很好地整合到临床卒中工作流程之前，建议未来的研究在大规模、多供应商数据上训练ML模型，在外部队列中进行验证，并采用正式的报告标准以提高模型的准确性和稳健性。
