Role of Deep Learning in Predicting Aging-Related Diseases: A Scoping Review

Aging refers to progressive physiological changes in a cell, an organ, or the whole body of an individual, over time. Aging-related diseases are highly prevalent and could impact an individual’s physical health. Recently, artificial intelligence (AI) methods have been used to predict aging-related d...

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Autores principales:	Jyotsna Talreja Wassan, Huiru Zheng, Haiying Wang
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	aging deep learning classification prediction PRISMA Biology (General) QH301-705.5
Acceso en línea:	https://doaj.org/article/4cef21b44c2a4856a0dc9d12ce87aa43
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spelling	oai:doaj.org-article:4cef21b44c2a4856a0dc9d12ce87aa432021-11-25T17:09:00ZRole of Deep Learning in Predicting Aging-Related Diseases: A Scoping Review10.3390/cells101129242073-4409https://doaj.org/article/4cef21b44c2a4856a0dc9d12ce87aa432021-10-01T00:00:00Zhttps://www.mdpi.com/2073-4409/10/11/2924https://doaj.org/toc/2073-4409Aging refers to progressive physiological changes in a cell, an organ, or the whole body of an individual, over time. Aging-related diseases are highly prevalent and could impact an individual’s physical health. Recently, artificial intelligence (AI) methods have been used to predict aging-related diseases and issues, aiding clinical providers in decision-making based on patient’s medical records. Deep learning (DL), as one of the most recent generations of AI technologies, has embraced rapid progress in the early prediction and classification of aging-related issues. In this paper, a scoping review of publications using DL approaches to predict common aging-related diseases (such as age-related macular degeneration, cardiovascular and respiratory diseases, arthritis, Alzheimer’s and lifestyle patterns related to disease progression), was performed. Google Scholar, IEEE and PubMed are used to search DL papers on common aging-related issues published between January 2017 and August 2021. These papers were reviewed, evaluated, and the findings were summarized. Overall, 34 studies met the inclusion criteria. These studies indicate that DL could help clinicians in diagnosing disease at its early stages by mapping diagnostic predictions into observable clinical presentations; and achieving high predictive performance (e.g., more than 90% accurate predictions of diseases in aging).Jyotsna Talreja WassanHuiru ZhengHaiying WangMDPI AGarticleagingdeep learningclassificationpredictionPRISMABiology (General)QH301-705.5ENCells, Vol 10, Iss 2924, p 2924 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	aging deep learning classification prediction PRISMA Biology (General) QH301-705.5
spellingShingle	aging deep learning classification prediction PRISMA Biology (General) QH301-705.5 Jyotsna Talreja Wassan Huiru Zheng Haiying Wang Role of Deep Learning in Predicting Aging-Related Diseases: A Scoping Review
description	Aging refers to progressive physiological changes in a cell, an organ, or the whole body of an individual, over time. Aging-related diseases are highly prevalent and could impact an individual’s physical health. Recently, artificial intelligence (AI) methods have been used to predict aging-related diseases and issues, aiding clinical providers in decision-making based on patient’s medical records. Deep learning (DL), as one of the most recent generations of AI technologies, has embraced rapid progress in the early prediction and classification of aging-related issues. In this paper, a scoping review of publications using DL approaches to predict common aging-related diseases (such as age-related macular degeneration, cardiovascular and respiratory diseases, arthritis, Alzheimer’s and lifestyle patterns related to disease progression), was performed. Google Scholar, IEEE and PubMed are used to search DL papers on common aging-related issues published between January 2017 and August 2021. These papers were reviewed, evaluated, and the findings were summarized. Overall, 34 studies met the inclusion criteria. These studies indicate that DL could help clinicians in diagnosing disease at its early stages by mapping diagnostic predictions into observable clinical presentations; and achieving high predictive performance (e.g., more than 90% accurate predictions of diseases in aging).
format	article
author	Jyotsna Talreja Wassan Huiru Zheng Haiying Wang
author_facet	Jyotsna Talreja Wassan Huiru Zheng Haiying Wang
author_sort	Jyotsna Talreja Wassan
title	Role of Deep Learning in Predicting Aging-Related Diseases: A Scoping Review
title_short	Role of Deep Learning in Predicting Aging-Related Diseases: A Scoping Review
title_full	Role of Deep Learning in Predicting Aging-Related Diseases: A Scoping Review
title_fullStr	Role of Deep Learning in Predicting Aging-Related Diseases: A Scoping Review
title_full_unstemmed	Role of Deep Learning in Predicting Aging-Related Diseases: A Scoping Review
title_sort	role of deep learning in predicting aging-related diseases: a scoping review
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/4cef21b44c2a4856a0dc9d12ce87aa43
work_keys_str_mv	AT jyotsnatalrejawassan roleofdeeplearninginpredictingagingrelateddiseasesascopingreview AT huiruzheng roleofdeeplearninginpredictingagingrelateddiseasesascopingreview AT haiyingwang roleofdeeplearninginpredictingagingrelateddiseasesascopingreview
_version_	1718412646586253312

Role of Deep Learning in Predicting Aging-Related Diseases: A Scoping Review

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