Deep Learning in mHealth for Cardiovascular Disease, Diabetes, and Cancer: Systematic Review
BackgroundMajor 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...
| Main Authors: | , , , , , , , , |
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| Format: | Article |
| Language: | English |
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JMIR Publications
2022-04-01
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| Series: | JMIR mHealth and uHealth |
| Online Access: | https://mhealth.jmir.org/2022/4/e32344 |
| _version_ | 1797735228623552512 |
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| author | Andreas Triantafyllidis Haridimos Kondylakis Dimitrios Katehakis Angelina Kouroubali Lefteris Koumakis Kostas Marias Anastasios Alexiadis Konstantinos Votis Dimitrios Tzovaras |
| author_facet | Andreas Triantafyllidis Haridimos Kondylakis Dimitrios Katehakis Angelina Kouroubali Lefteris Koumakis Kostas Marias Anastasios Alexiadis Konstantinos Votis Dimitrios Tzovaras |
| author_sort | Andreas Triantafyllidis |
| collection | DOAJ |
| description |
BackgroundMajor 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.
ObjectiveThe 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.
MethodsA 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.
ResultsIn 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.
ConclusionsThe 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. |
| first_indexed | 2024-03-12T12:55:14Z |
| format | Article |
| id | doaj.art-da505cc98c054f6fa0b63ddd012b51ea |
| institution | Directory Open Access Journal |
| issn | 2291-5222 |
| language | English |
| last_indexed | 2024-03-12T12:55:14Z |
| publishDate | 2022-04-01 |
| publisher | JMIR Publications |
| record_format | Article |
| series | JMIR mHealth and uHealth |
| spelling | doaj.art-da505cc98c054f6fa0b63ddd012b51ea2023-08-28T21:19:33ZengJMIR PublicationsJMIR mHealth and uHealth2291-52222022-04-01104e3234410.2196/32344Deep Learning in mHealth for Cardiovascular Disease, Diabetes, and Cancer: Systematic ReviewAndreas Triantafyllidishttps://orcid.org/0000-0002-6938-8256Haridimos Kondylakishttps://orcid.org/0000-0002-9917-4486Dimitrios Katehakishttps://orcid.org/0000-0002-3763-191XAngelina Kouroubalihttps://orcid.org/0000-0002-3023-8242Lefteris Koumakishttps://orcid.org/0000-0002-8442-4630Kostas Mariashttps://orcid.org/0000-0003-3783-5223Anastasios Alexiadishttps://orcid.org/0000-0001-7993-4225Konstantinos Votishttps://orcid.org/0000-0001-6381-8326Dimitrios Tzovarashttps://orcid.org/0000-0001-6915-6722 BackgroundMajor 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. ObjectiveThe 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. MethodsA 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. ResultsIn 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. ConclusionsThe 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.https://mhealth.jmir.org/2022/4/e32344 |
| spellingShingle | Andreas Triantafyllidis Haridimos Kondylakis Dimitrios Katehakis Angelina Kouroubali Lefteris Koumakis Kostas Marias Anastasios Alexiadis Konstantinos Votis Dimitrios Tzovaras Deep Learning in mHealth for Cardiovascular Disease, Diabetes, and Cancer: Systematic Review JMIR mHealth and uHealth |
| title | Deep Learning in mHealth for Cardiovascular Disease, Diabetes, and Cancer: Systematic Review |
| title_full | Deep Learning in mHealth for Cardiovascular Disease, Diabetes, and Cancer: Systematic Review |
| title_fullStr | Deep Learning in mHealth for Cardiovascular Disease, Diabetes, and Cancer: Systematic Review |
| title_full_unstemmed | Deep Learning in mHealth for Cardiovascular Disease, Diabetes, and Cancer: Systematic Review |
| title_short | Deep Learning in mHealth for Cardiovascular Disease, Diabetes, and Cancer: Systematic Review |
| title_sort | deep learning in mhealth for cardiovascular disease diabetes and cancer systematic review |
| url | https://mhealth.jmir.org/2022/4/e32344 |
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