Sharma Khushi, Shanbhog Manjula, Singh Kuljeet
Department of Computer Science, School of Sciences, Christ University, Delhi-NCR 201003, India.
Asian J Psychiatr. 2025 Jul;109:104537. doi: 10.1016/j.ajp.2025.104537. Epub 2025 May 20.
In recent years, machine learning and deep learning have shown potential for improving Parkinson's disease (PD) diagnosis, one of the most common neurodegenerative diseases. This comprehensive analysis examines machine learning and deep learning-based Parkinson's disease diagnosis using MRI, speech, and handwriting datasets. To thoroughly analyze PD, this study collected data from scientific literature, experimental investigations, publicly accessible datasets, and global health reports. This study examines the worldwide historical setting of Parkinson's disease, focusing on its increasing prevalence and inequities in treatment access across various regions. A comprehensive summary consolidates essential findings from clinical investigations and pertinent datasets related to Parkinson's disease management. The worldwide context, prospective treatments, therapies, and drugs for Parkinson's disease have been thoroughly examined. This analysis identifies significant research deficiencies and suggests future methods, emphasizing the necessity for more extensive and diverse datasets and improved model accessibility. The current study proposes the Meta-Park model for diagnosing Parkinson's disease, achieving training, testing, and validation accuracy of 97.67 %, 95 %, and 94.04 %. This method provides a dependable and scalable way to improve clinical decision-making in managing Parkinson's disease. This research seeks to provide innovative, data-driven decisions for early diagnosis and effective treatment by merging the proposed method with a thorough examination of existing interventions, providing renewed hope to patients and the medical community.
近年来，机器学习和深度学习已显示出改善帕金森病（PD）诊断的潜力，帕金森病是最常见的神经退行性疾病之一。这项综合分析使用磁共振成像（MRI）、语音和笔迹数据集，研究基于机器学习和深度学习的帕金森病诊断。为了全面分析帕金森病，本研究从科学文献、实验研究、可公开获取的数据集以及全球健康报告中收集数据。本研究考察了帕金森病的全球历史背景，重点关注其在各地区日益增加的患病率以及治疗可及性方面的不平等。一份全面的总结整合了临床研究以及与帕金森病管理相关的相关数据集的重要发现。对帕金森病的全球背景、前瞻性治疗方法、疗法和药物进行了全面研究。该分析确定了重大的研究不足，并提出了未来的方法，强调需要更广泛和多样的数据集以及提高模型的可及性。当前的研究提出了用于诊断帕金森病的Meta-Park模型，其训练、测试和验证准确率分别达到97.67%、95%和94.04%。该方法为改善帕金森病管理中的临床决策提供了一种可靠且可扩展的方式。本研究旨在通过将所提出的方法与对现有干预措施的全面审查相结合，为早期诊断和有效治疗提供创新的、数据驱动的决策，为患者和医学界带来新的希望。
