Shokrpour Sahar, MoghadamFarid AmirMehdi, Bazzaz Abkenar Sepideh, Haghi Kashani Mostafa, Akbari Mohammad, Sarvizadeh Mostafa
Department of Computer Engineering, ST.C., Islamic Azad University, Tehran, Iran.
Department of Computer Science, Michigan Technological University, Houghton, MI, USA.
NPJ Parkinsons Dis. 2025 Jul 1;11(1):187. doi: 10.1038/s41531-025-01025-9.
Parkinson's disease (PD) is a devastating neurological ailment affecting both mobility and cognitive function, posing considerable problems to the health of the elderly across the world. The absence of a conclusive treatment underscores the requirement to investigate cutting-edge diagnostic techniques to improve patient outcomes. Machine learning (ML) has the potential to revolutionize PD detection by applying large repositories of structured data to enhance diagnostic accuracy. 133 papers published between 2021 and April 2024 were reviewed using a systematic literature review (SLR) methodology, and subsequently classified into five categories: acoustic data, biomarkers, medical imaging, movement data, and multimodal datasets. This comprehensive analysis offers valuable insights into the applications of ML in PD diagnosis. Our SLR identifies the datasets and ML algorithms used for PD diagnosis, as well as their merits, limitations, and evaluation factors. We also discuss challenges, future directions, and outstanding issues.
帕金森病（PD）是一种严重的神经系统疾病，会影响运动和认知功能，给全球老年人的健康带来诸多问题。由于缺乏决定性的治疗方法，因此需要研究前沿诊断技术以改善患者预后。机器学习（ML）有潜力通过应用大量结构化数据存储库来提高诊断准确性，从而彻底改变帕金森病的检测方式。我们采用系统文献综述（SLR）方法对2021年至2024年4月期间发表的133篇论文进行了综述，随后将其分为五类：声学数据、生物标志物、医学成像、运动数据和多模态数据集。这一全面分析为机器学习在帕金森病诊断中的应用提供了有价值的见解。我们的系统文献综述确定了用于帕金森病诊断的数据集和机器学习算法，以及它们的优点、局限性和评估因素。我们还讨论了挑战、未来方向和未解决的问题。
