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A scoping review of neurodegenerative manifestations in explainable digital phenotyping
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...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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Nature Publishing Group UK
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10063633/ https://www.ncbi.nlm.nih.gov/pubmed/36997573 http://dx.doi.org/10.1038/s41531-023-00494-0 |
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author | Alfalahi, Hessa Dias, Sofia B. Khandoker, Ahsan H. Chaudhuri, Kallol Ray Hadjileontiadis, Leontios J. |
author_facet | Alfalahi, Hessa Dias, Sofia B. Khandoker, Ahsan H. Chaudhuri, Kallol Ray Hadjileontiadis, Leontios J. |
author_sort | Alfalahi, Hessa |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-10063633 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-100636332023-04-01 A scoping review of neurodegenerative manifestations in explainable digital phenotyping Alfalahi, Hessa Dias, Sofia B. Khandoker, Ahsan H. Chaudhuri, Kallol Ray Hadjileontiadis, Leontios J. NPJ Parkinsons Dis Review Article 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. Nature Publishing Group UK 2023-03-30 /pmc/articles/PMC10063633/ /pubmed/36997573 http://dx.doi.org/10.1038/s41531-023-00494-0 Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Review Article Alfalahi, Hessa Dias, Sofia B. Khandoker, Ahsan H. Chaudhuri, Kallol Ray Hadjileontiadis, Leontios J. A scoping review of neurodegenerative manifestations in explainable digital phenotyping |
title | A scoping review of neurodegenerative manifestations in explainable digital phenotyping |
title_full | A scoping review of neurodegenerative manifestations in explainable digital phenotyping |
title_fullStr | A scoping review of neurodegenerative manifestations in explainable digital phenotyping |
title_full_unstemmed | A scoping review of neurodegenerative manifestations in explainable digital phenotyping |
title_short | A scoping review of neurodegenerative manifestations in explainable digital phenotyping |
title_sort | scoping review of neurodegenerative manifestations in explainable digital phenotyping |
topic | Review Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10063633/ https://www.ncbi.nlm.nih.gov/pubmed/36997573 http://dx.doi.org/10.1038/s41531-023-00494-0 |
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