Department of Neurology, University Medical Center Groningen, University of Groningen, P.O. Box 30001, 9700 RB Groningen, The Netherlands; Data Science Center in Health, University Medical Center Groningen, University of Groningen, P.O. Box 30001, 9700 RB Groningen, The Netherlands.
Data Science Center in Health, University Medical Center Groningen, University of Groningen, P.O. Box 30001, 9700 RB Groningen, The Netherlands.
Artif Intell Med. 2024 Oct;156:102952. doi: 10.1016/j.artmed.2024.102952. Epub 2024 Aug 14.
The advent of computer vision technology and increased usage of video cameras in clinical settings have facilitated advancements in movement disorder analysis. This review investigated these advancements in terms of providing practical, low-cost solutions for the diagnosis and analysis of movement disorders, such as Parkinson's disease, ataxia, dyskinesia, and Tourette syndrome. Traditional diagnostic methods for movement disorders are typically reliant on the subjective assessment of motor symptoms, which poses inherent challenges. Furthermore, early symptoms are often overlooked, and overlapping symptoms across diseases can complicate early diagnosis. Consequently, deep learning has been used for the objective video-based analysis of movement disorders. This study systematically reviewed the latest advancements in automatic two-dimensional & three-dimensional video analysis using deep learning for movement disorders. We comprehensively analyzed the literature published until September 2023 by searching the Web of Science, PubMed, Scopus, and Embase databases. We identified 68 relevant studies and extracted information on their objectives, datasets, modalities, and methodologies. The study aimed to identify, catalogue, and present the most significant advancements, offering a consolidated knowledge base on the role of video analysis and deep learning in movement disorder analysis. First, the objectives, including specific PD symptom quantification, ataxia assessment, cerebral palsy assessment, gait disorder analysis, tremor assessment, tic detection (in the context of Tourette syndrome), dystonia assessment, and abnormal movement recognition were discussed. Thereafter, the datasets used in the study were examined. Subsequently, video modalities and deep learning methodologies related to the topic were investigated. Finally, the challenges and opportunities in terms of datasets, interpretability, evaluation methods, and home/remote monitoring were discussed.
计算机视觉技术的出现以及临床环境中视频摄像机的广泛使用，推动了运动障碍分析领域的进展。本综述探讨了这些进展，旨在为运动障碍（如帕金森病、共济失调、运动障碍和妥瑞氏综合征）的诊断和分析提供实用、低成本的解决方案。传统的运动障碍诊断方法通常依赖于对运动症状的主观评估，这存在固有挑战。此外，早期症状往往容易被忽视，而且疾病之间的症状重叠会使早期诊断变得复杂。因此，深度学习已被用于运动障碍的客观视频分析。本研究系统地综述了使用深度学习进行运动障碍的自动二维和三维视频分析的最新进展。我们通过检索 Web of Science、PubMed、Scopus 和 Embase 数据库，全面分析了截至 2023 年 9 月发表的最新文献。我们确定了 68 项相关研究，并提取了关于其目标、数据集、模态和方法的信息。本研究旨在识别、分类和呈现最重要的进展，为视频分析和深度学习在运动障碍分析中的作用提供一个综合的知识库。首先，讨论了研究的目标，包括特定的帕金森病症状量化、共济失调评估、脑瘫评估、步态障碍分析、震颤评估、妥瑞氏综合征中的抽搐检测、肌张力障碍评估和异常运动识别。然后，研究了使用的数据集。接着，研究了与主题相关的视频模态和深度学习方法。最后，讨论了数据集、可解释性、评估方法以及家庭/远程监测方面的挑战和机遇。
