Alfalahi Hessa, Dias Sofia B, Khandoker Ahsan H, Chaudhuri Kallol Ray, Hadjileontiadis Leontios J
Department of Biomedical Engineering, Khalifa University of Science and Technology, Abu Dhabi, United Arab Emirates.
Healthcare Engineering Innovation Center (HEIC), Khalifa University of Science and Technology, Abu Dhabi, United Arab Emirates.
NPJ Parkinsons Dis. 2023 Mar 30;9(1):49. doi: 10.1038/s41531-023-00494-0.
Neurologists nowadays no longer view neurodegenerative diseases, like Parkinson's and Alzheimer's disease, as single entities, but rather as a spectrum of multifaceted symptoms with heterogeneous progression courses and treatment responses. The definition of the naturalistic behavioral repertoire of early neurodegenerative manifestations is still elusive, impeding early diagnosis and intervention. Central to this view is the role of artificial intelligence (AI) in reinforcing the depth of phenotypic information, thereby supporting the paradigm shift to precision medicine and personalized healthcare. This suggestion advocates the definition of disease subtypes in a new biomarker-supported nosology framework, yet without empirical consensus on standardization, reliability and interpretability. Although the well-defined neurodegenerative processes, linked to a triad of motor and non-motor preclinical symptoms, are detected by clinical intuition, we undertake an unbiased data-driven approach to identify different patterns of neuropathology distribution based on the naturalistic behavior data inherent to populations in-the-wild. We appraise the role of remote technologies in the definition of digital phenotyping specific to brain-, body- and social-level neurodegenerative subtle symptoms, emphasizing inter- and intra-patient variability powered by deep learning. As such, the present review endeavors to exploit digital technologies and AI to create disease-specific phenotypic explanations, facilitating the understanding of neurodegenerative diseases as "bio-psycho-social" conditions. Not only does this translational effort within explainable digital phenotyping foster the understanding of disease-induced traits, but it also enhances diagnostic and, eventually, treatment personalization.
如今，神经学家不再将帕金森病和阿尔茨海默病等神经退行性疾病视为单一实体，而是将其视为一系列具有多方面症状、进展过程各异且治疗反应不同的疾病。早期神经退行性表现的自然行为表现形式的定义仍然难以捉摸，这阻碍了早期诊断和干预。这一观点的核心是人工智能（AI）在强化表型信息深度方面的作用，从而支持向精准医学和个性化医疗的范式转变。这一建议主张在一个新的生物标志物支持的疾病分类框架中定义疾病亚型，但在标准化、可靠性和可解释性方面尚未达成实证共识。尽管与运动和非运动临床前症状三联征相关的明确神经退行性过程可通过临床直觉检测到，但我们采用一种无偏的数据驱动方法，根据野生人群固有的自然行为数据来识别神经病理学分布的不同模式。我们评估远程技术在定义特定于大脑、身体和社会层面神经退行性细微症状的数字表型方面的作用，强调由深度学习驱动的患者间和患者内变异性。因此，本综述致力于利用数字技术和人工智能来创建特定疾病的表型解释，促进将神经退行性疾病理解为“生物 - 心理 - 社会”状况。这种在可解释数字表型内的转化努力不仅有助于理解疾病诱导的特征，还能增强诊断并最终实现治疗个性化。
