Artificial intelligence/machine learning for neuroimaging to predict hemorrhagic transformation: Systematic review/meta-analysis

IF 2.3 4区医学 Q3 CLINICAL NEUROLOGY

Journal of Neuroimaging Pub Date : 2024-07-21 DOI:10.1111/jon.13223

Richard Dagher, Burak Berksu Ozkara, Mert Karabacak, Samir A. Dagher, Elijah Isaac Rumbaut, Licia P. Luna, Vivek S. Yedavalli, Max Wintermark

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引用次数: 0

Abstract

Background and Purpose

Early and reliable prediction of hemorrhagic transformation (HT) in patients with acute ischemic stroke (AIS) is crucial for treatment decisions and early intervention. The purpose of this study was to conduct a systematic review and meta-analysis on the performance of artificial intelligence (AI) and machine learning (ML) models that utilize neuroimaging to predict HT.

Methods

A systematic search of PubMed, EMBASE, and Web of Science was conducted until February 19, 2024. Inclusion criteria were as follows: patients with AIS who received reperfusion therapy; AI/ML algorithm using imaging to predict HT; or presence of sufficient data on the predictive performance. Exclusion criteria were as follows: articles with less than 20 patients; articles lacking algorithms that operate solely on images; or articles not detailing the algorithm used. The quality of eligible studies was assessed using the Quality Assessment of Diagnostic Accuracy Studies-2 and Checklist for Artificial Intelligence in Medical Imaging. Pooled sensitivity, specificity, and diagnostic odds ratio (DOR) were calculated using a random-effects model, and a summary receiver operating characteristic curve was constructed using the Reitsma method.

Results

We identified six eligible studies, which included 1640 patients. Aside from an unclear risk of bias regarding flow and timing identified in two of the studies, all studies showed low risk of bias and applicability concerns in all categories. Pooled sensitivity, specificity, and DOR were .849, .878, and 45.598, respectively.

Conclusion

AI/ML models can reliably predict the occurrence of HT in AIS patients. More prospective studies are needed for subgroup analyses and higher clinical certainty and usefulness.

Abstract Image

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人工智能/机器学习用于神经影像学预测出血转化：系统综述/元分析。

背景和目的：早期可靠地预测急性缺血性卒中（AIS）患者的出血转化（HT）对于治疗决策和早期干预至关重要。本研究旨在对利用神经影像学预测出血性转变的人工智能（AI）和机器学习（ML）模型的性能进行系统回顾和荟萃分析：方法：对PubMed、EMBASE和Web of Science进行了系统检索，直至2024年2月19日。纳入标准如下：接受再灌注治疗的 AIS 患者；利用影像学预测 HT 的 AI/ML 算法；或有关于预测性能的充足数据。排除标准如下：患者人数少于 20 人的文章；缺乏完全基于图像的算法的文章；或未详细说明所用算法的文章。符合条件的研究采用诊断准确性研究质量评估-2 和医学影像人工智能检查表进行质量评估。使用随机效应模型计算汇总的灵敏度、特异性和诊断几率比（DOR），并使用Reitsma方法构建接收者操作特征曲线：我们确定了六项符合条件的研究，共纳入了 1640 名患者。除了其中两项研究在流程和时间方面存在不明确的偏倚风险外，所有研究在所有类别中均显示出较低的偏倚风险和适用性问题。汇总的敏感性、特异性和DOR分别为0.849、0.878和45.598：结论：AI/ML 模型可以可靠地预测 AIS 患者 HT 的发生。结论：AI/ML 模型可以可靠地预测 AIS 患者高血压的发生，但还需要更多的前瞻性研究来进行亚组分析，以提高临床确定性和实用性。

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来源期刊

Journal of Neuroimaging 医学-核医学

CiteScore

4.70

自引率

0.00%

发文量

117

审稿时长

6-12 weeks

期刊介绍： Start reading the Journal of Neuroimaging to learn the latest neurological imaging techniques. The peer-reviewed research is written in a practical clinical context, giving you the information you need on: MRI CT Carotid Ultrasound and TCD SPECT PET Endovascular Surgical Neuroradiology Functional MRI Xenon CT and other new and upcoming neuroscientific modalities.The Journal of Neuroimaging addresses the full spectrum of human nervous system disease, including stroke, neoplasia, degenerating and demyelinating disease, epilepsy, tumors, lesions, infectious disease, cerebral vascular arterial diseases, toxic-metabolic disease, psychoses, dementias, heredo-familial disease, and trauma.Offering original research, review articles, case reports, neuroimaging CPCs, and evaluations of instruments and technology relevant to the nervous system, the Journal of Neuroimaging focuses on useful clinical developments and applications, tested techniques and interpretations, patient care, diagnostics, and therapeutics. Start reading today!