TY  - JOUR
AU  - Triantafyllidis, Andreas
AU  - Kondylakis, Haridimos
AU  - Katehakis, Dimitrios
AU  - Kouroubali, Angelina
AU  - Koumakis, Lefteris
AU  - Marias, Kostas
AU  - Alexiadis, Anastasios
AU  - Votis, Konstantinos
AU  - Tzovaras, Dimitrios
PY  - 2022
DA  - 2022/4/4
TI  - Deep Learning in mHealth for Cardiovascular Disease, Diabetes, and Cancer: Systematic Review
JO  - JMIR Mhealth Uhealth
SP  - e32344
VL  - 10
IS  - 4
KW  - mHealth
KW  - deep learning
KW  - chronic disease
KW  - review
KW  - mobile phone
AB  - Background: Major chronic diseases such as cardiovascular disease (CVD), diabetes, and cancer impose a significant burden on people and health care systems around the globe. Recently, deep learning (DL) has shown great potential for the development of intelligent mobile health (mHealth) interventions for chronic diseases that could revolutionize the delivery of health care anytime, anywhere. Objective: The aim of this study is to present a systematic review of studies that have used DL based on mHealth data for the diagnosis, prognosis, management, and treatment of major chronic diseases and advance our understanding of the progress made in this rapidly developing field. Methods: A search was conducted on the bibliographic databases Scopus and PubMed to identify papers with a focus on the deployment of DL algorithms that used data captured from mobile devices (eg, smartphones, smartwatches, and other wearable devices) targeting CVD, diabetes, or cancer. The identified studies were synthesized according to the target disease, the number of enrolled participants and their age, and the study period as well as the DL algorithm used, the main DL outcome, the data set used, the features selected, and the achieved performance. Results: In total, 20 studies were included in the review. A total of 35% (7/20) of DL studies targeted CVD, 45% (9/20) of studies targeted diabetes, and 20% (4/20) of studies targeted cancer. The most common DL outcome was the diagnosis of the patient’s condition for the CVD studies, prediction of blood glucose levels for the studies in diabetes, and early detection of cancer. Most of the DL algorithms used were convolutional neural networks in studies on CVD and cancer and recurrent neural networks in studies on diabetes. The performance of DL was found overall to be satisfactory, reaching >84% accuracy in most studies. In comparison with classic machine learning approaches, DL was found to achieve better performance in almost all studies that reported such comparison outcomes. Most of the studies did not provide details on the explainability of DL outcomes. Conclusions: The use of DL can facilitate the diagnosis, management, and treatment of major chronic diseases by harnessing mHealth data. Prospective studies are now required to demonstrate the value of applied DL in real-life mHealth tools and interventions. 
SN  - 2291-5222
UR  - https://mhealth.jmir.org/2022/4/e32344
UR  - https://doi.org/10.2196/32344
UR  - http://www.ncbi.nlm.nih.gov/pubmed/35377325
DO  - 10.2196/32344
ID  - info:doi/10.2196/32344
ER  -