Department of Neurological Surgery, Neurology and Critical Care, Mayo Clinic, Jacksonville, FL 32224, United States of America.
Mayo Clinic Graduate School of Biomedical Sciences, Mayo Clinic, Rochester, MN 55902, United States of America.
J Neurol Sci. 2023 Nov 15;454:120832. doi: 10.1016/j.jns.2023.120832. Epub 2023 Oct 10.
Aneurysmal subarachnoid hemorrhage (SAH) is a subtype of hemorrhagic stroke with thirty-day mortality as high as 40%. Given the expansion of Machine Learning (ML) and Artificial intelligence (AI) methods in health care, SAH patients desperately need an integrated AI system that detects, segments, and supports clinical decisions based on presentation and severity.
This review aims to synthesize the current state of the art of AI and ML tools for the management of SAH patients alongside providing an up-to-date account of future horizons in patient care.
We performed a systematic review through various databases such as Cochrane Central Register of Controlled Trials, MEDLINE, Scopus, Cochrane Database of Systematic Reviews, and Embase.
A total of 507 articles were identified. Following extensive revision, only 21 articles were relevant. Two studies reported improved mortality prediction using Glasgow Coma Scale and biomarkers such as Neutrophil to Lymphocyte Ratio and glucose. One study reported that ffANN is equal to the SAHIT and VASOGRADE scores. One study reported that metabolic biomarkers Ornithine, Symmetric Dimethylarginine, and Dimethylguanidine Valeric acid were associated with poor outcomes. Nine studies reported improved prediction of complications and reduction in latency until intervention using clinical scores and imaging. Four studies reported accurate prediction of aneurysmal rupture based on size, shape, and CNN. One study reported AI-assisted Robotic Transcranial Doppler as a substitute for clinicians.
AI/ML technologies possess tremendous potential in accelerating SAH systems-of-care. Keeping abreast of developments is vital in advancing timely interventions for critical diseases.
蛛网膜下腔出血（SAH）是出血性中风的一个亚型，其 30 天死亡率高达 40%。鉴于机器学习（ML）和人工智能（AI）方法在医疗保健领域的扩展，SAH 患者迫切需要一个集成的 AI 系统，该系统可以根据表现和严重程度进行检测、分割，并支持临床决策。
本综述旨在综合目前用于管理 SAH 患者的 AI 和 ML 工具的最新技术，并提供患者护理的未来前景的最新情况。
我们通过各种数据库（如 Cochrane 对照试验中心注册库、MEDLINE、Scopus、Cochrane 系统评价数据库和 Embase）进行了系统评价。
共确定了 507 篇文章。经过广泛修订，只有 21 篇文章是相关的。两项研究报告称，使用格拉斯哥昏迷量表和中性粒细胞与淋巴细胞比率和葡萄糖等生物标志物可以改善死亡率预测。一项研究报告称，ffANN 与 SAHIT 和 VASOGRADE 评分相等。一项研究报告称，代谢生物标志物精氨酸、对称二甲基精氨酸和二甲基胍戊酸与不良结局相关。九项研究报告称，使用临床评分和影像学可以改善并发症预测并减少干预潜伏期。四项研究报告称，基于大小、形状和 CNN 可以准确预测动脉瘤破裂。一项研究报告称，人工智能辅助机器人经颅多普勒超声可以替代临床医生。
AI/ML 技术在加速 SAH 系统护理方面具有巨大潜力。跟上最新发展对于推进危急疾病的及时干预至关重要。
