机器学习在轻度创伤性脑损伤弥散张量成像诊断和预后中的应用:系统综述。

IF 3.5 2区 医学 Q1 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING
Christian John A Saludar, Maryam Tayebi, Eryn Kwon, Joshua McGeown, William Schierding, Alan Wang, Justin Fernandez, Samantha Holdsworth, Vickie Shim
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引用次数: 0

摘要

背景:外伤性脑损伤(TBI)是一个全球性的健康问题,其中轻度TBI (mTBI)是最常见的形式。尽管mTBI很普遍,但准确诊断mTBI仍然是一个重大挑战。虽然像弥散张量成像(DTI)这样的先进神经成像技术为更可靠的诊断提供了希望,但它们的临床应用受到损伤后发现不一致和异质性的限制。最近,利用DTI指标作为特征的机器学习(ML)技术在mTBI研究中显示出越来越多的效用。这种方法有助于识别不同的组间特征,为更精确、更有效的诊断和预后工具铺平道路。目的:本综述旨在分析使用ML技术评估mTBI后DTI指标变化的研究。研究类型:系统评价。人群或受试者或幻影或标本或动物模型:我们根据PRISMA指南,对ML与DTI在人类受试者mTBI诊断和预后中的应用进行了系统回顾。本综述确定了36篇文章。场强/顺序:无。评估:使用改进的qualsystem评估工具评估研究质量。统计测试:无。结果:回顾发现使用DTI指标单独或与其他模式(即结构MRI,功能MRI,临床评分或人口统计学)的ML技术可以有效地将mTBI患者与对照组区分开来。这些方法也显示了根据恢复程度和症状严重程度对mTBI患者进行分类的潜力。此外,这些机器学习模型对认知得分和大脑结构衰退表现出很强的预测能力,通过大脑预测的年龄差异进行量化。数据结论:需要更大的、外部验证的研究来建立mTBI的诊断和预后的稳健模型,使用成像生物标志物(包括DTI)与非成像、现场或临床数据相结合。尽管ML算法具有很高的预测性能,但临床应用仍然很遥远,这可能是由于研究样本量小,缺乏外部验证,这引起了对过拟合的担忧。证据等级:5。技术功效:第一阶段。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Application of Machine Learning in the Diagnosis and Prognosis of Mild Traumatic Brain Injury Using Diffusion Tensor Imaging: A Systematic Review.

Background: Traumatic Brain Injury (TBI) is a global health concern, with mild TBI (mTBI) being the most common form. Despite its prevalence, accurately diagnosing mTBI remains a significant challenge. While advanced neuroimaging techniques like diffusion tensor imaging (DTI) offer promise for more robust diagnosis, their clinical application is limited by inconsistent and heterogeneous post-injury findings. Recently, machine learning (ML) techniques, utilizing DTI metrics as features, have shown increasing utility in mTBI research. This approach helps identify distinct between-group features, paving the way for more precise and efficient diagnostic and prognostic tools.

Purpose: This review aims to analyze studies employing ML techniques to assess changes in DTI metrics after mTBI.

Study type: Systematic review.

Population or subjects or phantom or specimen or animal model: We conducted a systematic review, adhering to PRISMA guidelines, on the application of ML with DTI for mTBI diagnosis and prognosis on human subjects. This review identified 36 articles.

Field strength/sequence: N/A.

Assessment: Study quality was assessed using the Modified QualSyst Assessment Tool.

Statistical tests: N/A.

Results: The review found ML techniques using DTI Metrics either alone or in combination with other modalities (i.e., structural MRI, functional MRI, clinical scores, or demographics) can effectively classify mTBI patients from controls. These approaches have also demonstrated potential in classifying mTBI patients according to the degree of recovery and symptom severity. In addition, these ML models showed strong predictive power toward cognitive scores and brain structural decline, as quantified by brain-predicted age difference.

Data conclusion: Larger, externally validated studies are needed to develop robust models for the diagnosis and prognosis of mTBI, using imaging biomarkers (including DTI) in conjunction with non-imaging, on-field, or clinical data. Despite the high predictive performance of ML algorithms, the clinical application remains distant, likely due to the small sample size of studies and lack of external validation, which raises concerns about overfitting.

Evidence level: 5.

Technical efficacy: Stage 1.

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来源期刊
CiteScore
9.70
自引率
6.80%
发文量
494
审稿时长
2 months
期刊介绍: The Journal of Magnetic Resonance Imaging (JMRI) is an international journal devoted to the timely publication of basic and clinical research, educational and review articles, and other information related to the diagnostic applications of magnetic resonance.
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