Vizcarra Joaquin A, Yarlagadda Sushuma, Xie Kevin, Ellis Colin A, Spindler Meredith, Hammer Lauren H
Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA.
Parkinson's Disease Research, Education and Clinical Center, Philadelphia Veterans Affairs Medical Center, Philadelphia, PA 19104, USA.
J Clin Med. 2024 Nov 21;13(23):7009. doi: 10.3390/jcm13237009.
: Hyperkinetic movement disorders involve excessive, involuntary movements such as ataxia, chorea, dystonia, myoclonus, tics, and tremor. Recent advances in artificial intelligence (AI) allow investigators to integrate multimodal instrumented movement measurements and imaging techniques and to analyze these data together at scale. In this systematic review, we aim to characterize AI's performance in diagnosing and quantitatively phenotyping these disorders. : We searched PubMed and Embase using a semi-automated article-screening pipeline. : Fifty-five studies met the inclusion criteria (n = 11,946 subjects). Thirty-five studies used machine learning, sixteen used deep learning, and four used both. Thirty-eight studies reported disease diagnosis, twenty-three reported quantitative phenotyping, and six reported both. Diagnostic accuracy was reported in 36 of 38 and correlation coefficients in 10 of 23 studies. Kinematics (e.g., accelerometers and inertial measurement units) were the most used dataset. Diagnostic accuracy was reported in 36 studies and ranged from 56 to 100% compared to clinical diagnoses to differentiate them from healthy controls. The correlation coefficient was reported in 10 studies and ranged from 0.54 to 0.99 compared to clinical ratings for quantitative phenotyping. Five studies had an overall judgment of "low risk of bias" and three had external validation. : There is a need to adopt AI-based research guidelines to minimize reporting heterogeneity and bolster clinical interpretability.
多动性运动障碍涉及过度的、不自主的运动，如共济失调、舞蹈症、肌张力障碍、肌阵挛、抽搐和震颤。人工智能（AI）的最新进展使研究人员能够整合多模态仪器化运动测量和成像技术，并大规模地一起分析这些数据。在本系统评价中，我们旨在描述AI在诊断这些疾病和进行定量表型分析方面的性能。
我们使用半自动文章筛选流程在PubMed和Embase中进行检索。
55项研究符合纳入标准（n = 11946名受试者）。35项研究使用机器学习，16项使用深度学习，4项同时使用两者。38项研究报告了疾病诊断，23项报告了定量表型分析，6项同时报告了两者。38项研究中的36项报告了诊断准确性，23项研究中的10项报告了相关系数。运动学（如加速度计和惯性测量单元）是最常用的数据集。36项研究报告了诊断准确性，与临床诊断相比，将其与健康对照区分开来的诊断准确性范围为56%至100%。10项研究报告了相关系数，与定量表型分析的临床评分相比，相关系数范围为0.54至0.99。5项研究的总体判断为“低偏倚风险”，3项进行了外部验证。
有必要采用基于AI的研究指南，以尽量减少报告的异质性并增强临床可解释性。
