Shusharina Natalia, Yukhnenko Denis, Botman Stepan, Sapunov Viktor, Savinov Vladimir, Kamyshov Gleb, Sayapin Dmitry, Voznyuk Igor
Baltic Center for Neurotechnologies and Artificial Intelligence, Immanuel Kant Baltic Federal University, 236041 Kaliningrad, Russia.
Department of Social Security and Humanitarian Technologies, N. I. Lobachevsky State University of Nizhny Novgorod, 603022 Nizhny Novgorod, Russia.
Diagnostics (Basel). 2023 Feb 3;13(3):573. doi: 10.3390/diagnostics13030573.
This paper discusses the promising areas of research into machine learning applications for the prevention and correction of neurodegenerative and depressive disorders. These two groups of disorders are among the leading causes of decline in the quality of life in the world when estimated using disability-adjusted years. Despite decades of research, the development of new approaches for the assessment (especially pre-clinical) and correction of neurodegenerative diseases and depressive disorders remains among the priority areas of research in neurophysiology, psychology, genetics, and interdisciplinary medicine. Contemporary machine learning technologies and medical data infrastructure create new research opportunities. However, reaching a consensus on the application of new machine learning methods and their integration with the existing standards of care and assessment is still a challenge to overcome before the innovations could be widely introduced to clinics. The research on the development of clinical predictions and classification algorithms contributes towards creating a unified approach to the use of growing clinical data. This unified approach should integrate the requirements of medical professionals, researchers, and governmental regulators. In the current paper, the current state of research into neurodegenerative and depressive disorders is presented.
本文探讨了机器学习在预防和纠正神经退行性疾病及抑郁症方面应用的研究前景。按伤残调整生命年估算，这两类疾病是全球生活质量下降的主要原因之一。尽管经过数十年研究，但开发用于评估（尤其是临床前评估）和纠正神经退行性疾病及抑郁症的新方法，仍是神经生理学、心理学、遗传学和跨学科医学研究的重点领域。当代机器学习技术和医学数据基础设施创造了新的研究机会。然而，在将这些创新广泛引入临床之前，就新机器学习方法的应用及其与现有护理和评估标准的整合达成共识，仍是一个有待克服的挑战。临床预测和分类算法开发方面的研究有助于创建一种统一方法来使用不断增长的临床数据。这种统一方法应整合医学专业人员、研究人员和政府监管机构的要求。本文介绍了神经退行性疾病和抑郁症的当前研究现状。
