Role of Deep Learning in Predicting Aging-Related Diseases: 2 A Scoping Review

Jyotsna Talreja Wassan, Huiru Zheng, Haiying Wang

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Abstract

Aging refers to progressive physiological changes in a cell, an organ, or the whole body 11 of an individual, over time. Aging-related diseases are highly prevalent and could impact an indi-12 vidual’s physical health. Recently, artificial intelligence (AI) methods have been used to predict ag-13 ing-related diseases and issues, aiding clinical providers in decision-making based on patient’s med-14 ical records. Deep learning (DL), as one of the most recent generations of AI technologies, has em-15 braced rapid progress in the early prediction and classification of aging-related issues. In this paper, 16 a scoping review of publications using DL approaches to predict common aging-related diseases 17 (such as age-related macular degeneration, cardiovascular and respiratory diseases, arthritis, Alz-18 heimer’s and lifestyle patterns related to disease progression), is performed. Google Scholar, IEEE 19 and PubMed are used to search DL papers on common aging-related issues published between Jan-20 uary 2017 and August 2021. These papers are reviewed, evaluated, and the findings are summa-21 rized. Overall, 34 studies met the inclusion criteria. These studies indicate that DL could help clini-22 cians in diagnosing disease at its early stages by mapping diagnostic predictions into observable 23 clinical presentations; and achieving high predictive performance (e.g., more than 90 % accurate 24 predictions of diseases in aging).
Original languageEnglish
Article numbere2924
JournalCells
Volume10
Issue number11
Early online date28 Oct 2021
DOIs
Publication statusE-pub ahead of print - 28 Oct 2021

Keywords

  • Aging
  • Deep Learning
  • Classification
  • Prediction
  • PRISMA

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