Role of deep learning in predicting aging-related diseases: A scoping review

Jyotsna Talreja Wassan, Huiru Zheng, Haiying Wang

Research output: Contribution to journalArticlepeer-review

4 Citations (Scopus)
108 Downloads (Pure)


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).

Original languageEnglish
Article number2924
Number of pages34
Issue number11
Early online date28 Oct 2021
Publication statusPublished online - 28 Oct 2021

Bibliographical note

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© 2021 by the authors. Licensee MDPI, Basel, Switzerland.


  • Aging
  • Deep Learning
  • Classification
  • Prediction


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