Applications of machine learning for computer-aided diagnosis of Parkinson’s disease: progress and benchmark case study

IF 13.9 2区计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Artificial Intelligence Review Pub Date : 2025-08-29 DOI:10.1007/s10462-025-11347-y

Juntao Zhang, Yiming Zhang, Ying Weng, Akram A. Hosseini, Boding Wang, Tom Dening, Weinyu Fan, Weizhong Xiao

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引用次数: 0

Abstract

Machine learning (ML) has emerged as a vital tool for the diagnosis of Parkinson’s Disease (PD). This study presents a comprehensive review on the applications of ML for computer-aided diagnosis (CAD) of PD. We conducted a comprehensive review by searching articles published from 2010 till 2024. The risk of bias is assessed using the PROBAST checklist. Case studies are also provided. This review includes 117 articles with six categories: neuroimaging data (20.5%); voice data (40.2%); handwriting data (12.0%); gait data (14.5%); EEG data (8.5%); and other data (4.3%). According to the PROBAST checklist, only 28 articles (23.9%) have a low risk of bias. A benchmark case study is conducted for five different data modalities. We also discuss current limitations and future directions of applying ML to the diagnosis of PD. This review reduces the gap between Artificial Intelligence (AI) and PD medical professionals and provides helpful information for future research.

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机器学习在帕金森病计算机辅助诊断中的应用：进展和基准案例研究

机器学习（ML）已经成为帕金森病（PD）诊断的重要工具。本文综述了机器学习在帕金森病计算机辅助诊断（CAD）中的应用。我们检索了从2010年到2024年发表的文章，进行了综合评价。使用PROBAST检查表评估偏倚风险。还提供了案例研究。本综述纳入117篇文献，分为6类：神经影像学资料（20.5%）；语音数据（40.2%）；手写数据（12.0%）；步态数据（14.5%）；EEG数据（8.5%）；其他数据（4.3%）。根据PROBAST检查表，只有28篇文章（23.9%）具有低偏倚风险。对五种不同的数据模式进行了基准案例研究。我们也讨论了目前的局限性和未来的方向应用机器学习诊断帕金森病。本文综述缩小了人工智能（AI）与PD医学专业人员之间的差距，并为未来的研究提供了有益的信息。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Artificial Intelligence Review 工程技术-计算机：人工智能

CiteScore

22.00

自引率

3.30%

发文量

194

审稿时长

5.3 months

期刊介绍： Artificial Intelligence Review, a fully open access journal, publishes cutting-edge research in artificial intelligence and cognitive science. It features critical evaluations of applications, techniques, and algorithms, providing a platform for both researchers and application developers. The journal includes refereed survey and tutorial articles, along with reviews and commentary on significant developments in the field.