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机器学习在神经退行性疾病诊断和治疗中的应用。

Applications of machine learning to diagnosis and treatment of neurodegenerative diseases.

机构信息

Sheffield Institute of Translational Neuroscience, University of Sheffield, Sheffield, UK.

BenevolentAI, London, UK.

出版信息

Nat Rev Neurol. 2020 Aug;16(8):440-456. doi: 10.1038/s41582-020-0377-8. Epub 2020 Jul 15.

Abstract

Globally, there is a huge unmet need for effective treatments for neurodegenerative diseases. The complexity of the molecular mechanisms underlying neuronal degeneration and the heterogeneity of the patient population present massive challenges to the development of early diagnostic tools and effective treatments for these diseases. Machine learning, a subfield of artificial intelligence, is enabling scientists, clinicians and patients to address some of these challenges. In this Review, we discuss how machine learning can aid early diagnosis and interpretation of medical images as well as the discovery and development of new therapies. A unifying theme of the different applications of machine learning is the integration of multiple high-dimensional sources of data, which all provide a different view on disease, and the automated derivation of actionable insights.

摘要

全球范围内,对于治疗神经退行性疾病的有效疗法存在巨大的未满足需求。神经元退化的分子机制的复杂性以及患者群体的异质性,为这些疾病的早期诊断工具和有效治疗方法的开发带来了巨大的挑战。机器学习是人工智能的一个分支,它使科学家、临床医生和患者能够应对其中的一些挑战。在这篇综述中,我们讨论了机器学习如何帮助早期诊断和解释医学图像,以及发现和开发新疗法。机器学习的不同应用的一个统一主题是整合多个多维数据源,这些数据源都提供了对疾病的不同视角,并自动得出可操作的见解。

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