机器学习在帕金森病诊断中的应用研究

Application of Machine Learning to Parkinson’s Disease Diagnosis

  • 摘要: 机器学习是医学人工智能的研究热点和重点之一。针对神经退行性帕金森病(Parkinson’s Disease, PD)的早期诊断,现有的临床评分量表具有一定的主观性和局限性。该文报告了基于行为(语音、步态、书写)、电生理(脑电)、影像组学(核磁共振成像、单光子发射断层成像、正光子发射断层成像)和基因组学等数据,机器学习应用于PD诊断的研究进展,发现其较传统方法更为精准,以期为未来人工智能智慧诊断的研究与应用提供参考与借鉴。

     

    Abstract: Machine learning is one of the research hotspots and focuses of medical artificial intelligence. For the early diagnosis of neurodegenerative Parkinson’s Disease (PD), the existing clinical rating scales have certain subjectivity and limitations. This paper reports the research progress of machine learning in the diagnosis of PD based on behavioral (speech, gait, and writing), electrophysiology (Electroencephalogram, EEG), radiomics (magnetic resonance imaging, single-photon emission tomography, and positive photon emission tomography), and genomics data. The report finds that the application of machine learning is more accurate than the traditional method in the diagnosis of PD, which provides reference for the research and application of artificial intelligence intelligent diagnosis in the future.

     

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