Dzialas Verena, Doering Elena, Eich Helena, Strafella Antonio P, Vaillancourt David E, Simonyan Kristina, van Eimeren Thilo
Department of Nuclear Medicine, Faculty of Medicine and University Hospital, University of Cologne, Cologne, Germany.
Faculty of Mathematics and Natural Sciences, University of Cologne, Cologne, Germany.
Mov Disord. 2024 Dec;39(12):2130-2143. doi: 10.1002/mds.30002. Epub 2024 Sep 5.
In recent years, many neuroimaging studies have applied artificial intelligence (AI) to facilitate existing challenges in Parkinson's disease (PD) diagnosis, prognosis, and intervention. The aim of this systematic review was to provide an overview of neuroimaging-based AI studies and to assess their methodological quality. A PubMed search yielded 810 studies, of which 244 that investigated the utility of neuroimaging-based AI for PD diagnosis, prognosis, or intervention were included. We systematically categorized studies by outcomes and rated them with respect to five minimal quality criteria (MQC) pertaining to data splitting, data leakage, model complexity, performance reporting, and indication of biological plausibility. We found that the majority of studies aimed to distinguish PD patients from healthy controls (54%) or atypical parkinsonian syndromes (25%), whereas prognostic or interventional studies were sparse. Only 20% of evaluated studies passed all five MQC, with data leakage, non-minimal model complexity, and reporting of biological plausibility as the primary factors for quality loss. Data leakage was associated with a significant inflation of accuracies. Very few studies employed external test sets (8%), where accuracy was significantly lower, and 19% of studies did not account for data imbalance. Adherence to MQC was low across all observed years and journal impact factors. This review outlines that AI has been applied to a wide variety of research questions pertaining to PD; however, the number of studies failing to pass the MQC is alarming. Therefore, we provide recommendations to enhance the interpretability, generalizability, and clinical utility of future AI applications using neuroimaging in PD. © 2024 The Author(s). Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.
近年来，许多神经影像学研究应用人工智能（AI）来应对帕金森病（PD）诊断、预后和干预方面现有的挑战。本系统综述的目的是概述基于神经影像学的AI研究，并评估其方法学质量。通过PubMed检索得到810项研究，其中244项研究被纳入，这些研究探讨了基于神经影像学的AI在PD诊断、预后或干预中的效用。我们根据研究结果对研究进行了系统分类，并根据与数据拆分、数据泄露、模型复杂性、性能报告以及生物学合理性指征相关的五个最低质量标准（MQC）对其进行评分。我们发现，大多数研究旨在区分PD患者与健康对照（54%）或非典型帕金森综合征（25%），而预后或干预性研究较少。在评估的研究中，只有20%通过了所有五项MQC，数据泄露、非最小模型复杂性以及生物学合理性报告是质量损失的主要因素。数据泄露与准确率的显著虚高有关。很少有研究采用外部测试集（8%），其准确率显著较低，19%的研究未考虑数据不平衡问题。在所有观察年份和期刊影响因子中，对MQC的遵守情况都很低。本综述概述了AI已被应用于与PD相关的各种研究问题；然而，未能通过MQC的研究数量令人担忧。因此，我们提供了一些建议，以提高未来使用神经影像学的AI在PD中的可解释性、可推广性和临床效用。© 2024作者。《运动障碍》由Wiley Periodicals LLC代表国际帕金森和运动障碍协会出版。
