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Result: Disease Prediction Using Machine Learning on Smartphone-Based Eye, Skin, and Voice Data: Scoping Review.

Title:
Disease Prediction Using Machine Learning on Smartphone-Based Eye, Skin, and Voice Data: Scoping Review.
Authors:
Dawadi R; Artificial Intelligence Center for Health and Biomedical Research, National Institutes of Biomedical Innovation, Health and Nutrition, Osaka, Japan.; National Cerebral and Cardiovascular Center, Osaka, Japan., Inoue M; Artificial Intelligence Center for Health and Biomedical Research, National Institutes of Biomedical Innovation, Health and Nutrition, Osaka, Japan.; National Cerebral and Cardiovascular Center, Osaka, Japan., Tay JT; Artificial Intelligence Center for Health and Biomedical Research, National Institutes of Biomedical Innovation, Health and Nutrition, Osaka, Japan.; National Cerebral and Cardiovascular Center, Osaka, Japan., Martin-Morales A; Artificial Intelligence Center for Health and Biomedical Research, National Institutes of Biomedical Innovation, Health and Nutrition, Osaka, Japan.; National Cerebral and Cardiovascular Center, Osaka, Japan., Vu T; Artificial Intelligence Center for Health and Biomedical Research, National Institutes of Biomedical Innovation, Health and Nutrition, Osaka, Japan.; National Cerebral and Cardiovascular Center, Osaka, Japan., Araki M; Artificial Intelligence Center for Health and Biomedical Research, National Institutes of Biomedical Innovation, Health and Nutrition, Osaka, Japan.; National Cerebral and Cardiovascular Center, Osaka, Japan.; Faculty of Medicine, Graduate School of Medicine, Kyoto University, Kyoto, Japan.; Graduate School of Science, Technology and Innovation, Kobe University, Kobe, Japan.
Source:
JMIR AI [JMIR AI] 2025 Mar 25; Vol. 4, pp. e59094. Date of Electronic Publication: 2025 Mar 25.
Publication Type:
Journal Article; Review
Language:
English
Journal Info:
Publisher: JMIR Publications Country of Publication: Canada NLM ID: 9918645789006676 Publication Model: Electronic Cited Medium: Internet ISSN: 2817-1705 (Electronic) Linking ISSN: 28171705 NLM ISO Abbreviation: JMIR AI Subsets: PubMed not MEDLINE
Imprint Name(s):
Original Publication: [Toronto, ON] : JMIR Publications, [2022]-
References:
Sci Rep. 2020 Sep 15;10(1):15100. (PMID: 32934246)
Multimed Tools Appl. 2022;81(10):14475-14501. (PMID: 35233182)
J Med Internet Res. 2023 Jan 25;25:e34474. (PMID: 36696160)
Healthc Inform Res. 2022 Jul;28(3):210-221. (PMID: 35982595)
Sensors (Basel). 2019 May 09;19(9):. (PMID: 31075985)
Comput Biol Med. 2022 Feb;141:105153. (PMID: 34954610)
Nat Commun. 2020 Sep 11;11(1):4553. (PMID: 32917902)
J Cancer Res Clin Oncol. 2022 Sep;148(9):2497-2505. (PMID: 34546412)
JAMA Dermatol. 2019 Jan 1;155(1):58-65. (PMID: 30484822)
J Grad Med Educ. 2022 Oct;14(5):565-567. (PMID: 36274762)
Comput Biol Med. 2022 Sep;148:105885. (PMID: 35930957)
BMJ Open. 2022 Apr 27;12(4):e059033. (PMID: 35477874)
Sensors (Basel). 2021 Oct 23;21(21):. (PMID: 34770345)
Ann Intern Med. 2009 Aug 18;151(4):264-9, W64. (PMID: 19622511)
Ann Clin Transl Neurol. 2019 Aug;6(8):1498-1509. (PMID: 31402628)
IEEE Internet Things J. 2021 Mar 22;8(21):15892-15905. (PMID: 35782187)
Sensors (Basel). 2022 Dec 30;23(1):. (PMID: 36617005)
Acta Psychiatr Scand. 2021 May;143(5):453-465. (PMID: 33354769)
Diagnostics (Basel). 2022 Aug 03;12(8):. (PMID: 36010229)
JAMA Pediatr. 2021 Aug 1;175(8):827-836. (PMID: 33900383)
Sleep Breath. 2019 Mar;23(1):243-250. (PMID: 30032464)
PLoS One. 2022 Jan 13;17(1):e0262448. (PMID: 35025945)
Data Brief. 2020 Aug 25;32:106221. (PMID: 32939378)
Comput Biol Med. 2023 Mar;155:106659. (PMID: 36791550)
BMC Med Inform Decis Mak. 2012 Jul 10;12:67. (PMID: 22781312)
BMC Bioinformatics. 2020 Jul 6;21(Suppl 4):259. (PMID: 32631221)
Pediatr Res. 2020 Feb;87(3):576-580. (PMID: 31585457)
Dermatol Pract Concept. 2019 Dec 31;10(1):e2020011. (PMID: 31921498)
Sensors (Basel). 2018 Nov 19;18(11):. (PMID: 30463199)
Biomed Eng Online. 2021 Nov 21;20(1):114. (PMID: 34802448)
JAMA Netw Open. 2019 Oct 2;2(10):e1913436. (PMID: 31617929)
Sensors (Basel). 2018 Nov 15;18(11):. (PMID: 30445798)
Int J Environ Res Public Health. 2017 Apr 19;14(4):. (PMID: 28422077)
Transl Vis Sci Technol. 2020 Dec 04;9(2):60. (PMID: 33294301)
Digit Biomark. 2019 Dec 10;3(3):166-175. (PMID: 32095775)
J Med Internet Res. 2023 Apr 14;25:e44410. (PMID: 36881540)
Ophthalmol Sci. 2022 Apr 15;2(3):100158. (PMID: 36245758)
Sci Rep. 2022 Jun 11;12(1):9687. (PMID: 35690657)
NPJ Parkinsons Dis. 2022 Oct 29;8(1):145. (PMID: 36309501)
JMIR Form Res. 2022 Aug 30;6(8):e37061. (PMID: 36040767)
Healthc Inform Res. 2021 Jul;27(3):189-199. (PMID: 34384201)
JMIR Serious Games. 2022 Aug 16;10(3):e39186. (PMID: 35972793)
PLoS One. 2017 Oct 5;12(10):e0185613. (PMID: 28982171)
IEEE Trans Biomed Eng. 2019 May;66(5):1402-1411. (PMID: 30403615)
Sci Data. 2020 Oct 16;7(1):354. (PMID: 33067468)
BMJ Open. 2017 Jun 23;7(6):e015462. (PMID: 28645967)
BMC Med Inform Decis Mak. 2019 Nov 6;19(1):211. (PMID: 31694707)
Brain Inform. 2020 Oct 22;7(1):12. (PMID: 33090328)
Front Psychiatry. 2020 Dec 18;11:584711. (PMID: 33391050)
JMIR Mhealth Uhealth. 2020 Sep 29;8(9):e17818. (PMID: 32990638)
EBioMedicine. 2019 May;43:107-113. (PMID: 31101596)
Healthc Inform Res. 2020 Oct;26(4):274-283. (PMID: 33190461)
Annu Int Conf IEEE Eng Med Biol Soc. 2022 Jul;2022:4599-4603. (PMID: 36085895)
Pediatr Pulmonol. 2022 Mar;57(3):761-767. (PMID: 34964557)
BMC Med Inform Decis Mak. 2019 Dec 21;19(1):281. (PMID: 31864346)
IEEE J Transl Eng Health Med. 2021 Apr 20;9:3800110. (PMID: 34786216)
BMC Health Serv Res. 2022 Oct 14;22(1):1247. (PMID: 36242021)
BMJ Open. 2022 Nov 22;12(11):e062463. (PMID: 36414294)
BioData Min. 2021 Feb 2;14(1):11. (PMID: 33531048)
JAMA Ophthalmol. 2022 Feb 01;140(2):153-160. (PMID: 34913967)
Int J Bipolar Disord. 2021 Dec 1;9(1):38. (PMID: 34850296)
JAMA Neurol. 2018 Jul 1;75(7):876-880. (PMID: 29582075)
Front Psychol. 2022 Apr 08;13:811517. (PMID: 35478769)
JMIR Mhealth Uhealth. 2022 Jan 24;10(1):e31857. (PMID: 35072646)
Sensors (Basel). 2015 Sep 17;15(9):23653-66. (PMID: 26393591)
Hypertension. 2008 Oct;52(4):652-9. (PMID: 18725580)
Behav Res Methods. 2023 Sep;55(6):3149-3163. (PMID: 36070130)
Ir J Med Sci. 2018 May;187(2):501-513. (PMID: 28756541)
BMJ Open. 2021 Oct 22;11(10):e055356. (PMID: 34686559)
Contributed Indexing:
Keywords: health diagnosis; literature review; machine learning; smartphone
Entry Date(s):
Date Created: 20250325 Latest Revision: 20250411
Update Code:
20250414
PubMed Central ID:
PMC11979540
DOI:
10.2196/59094
PMID:
40132187
Database:
MEDLINE

Further information

Background: The application of machine learning methods to data generated by ubiquitous devices like smartphones presents an opportunity to enhance the quality of health care and diagnostics. Smartphones are ideal for gathering data easily, providing quick feedback on diagnoses, and proposing interventions for health improvement.
Objective: We reviewed the existing literature to gather studies that have used machine learning models with smartphone-derived data for the prediction and diagnosis of health anomalies. We divided the studies into those that used machine learning models by conducting experiments to retrieve data and predict diseases, and those that used machine learning models on publicly available databases. The details of databases, experiments, and machine learning models are intended to help researchers working in the fields of machine learning and artificial intelligence in the health care domain. Researchers can use the information to design their experiments or determine the databases they could analyze.
Methods: A comprehensive search of the PubMed and IEEE Xplore databases was conducted, and an in-house keyword screening method was used to filter the articles based on the content of their titles and abstracts. Subsequently, studies related to the 3 areas of voice, skin, and eye were selected and analyzed based on how data for machine learning models were extracted (ie, the use of publicly available databases or through experiments). The machine learning methods used in each study were also noted.
Results: A total of 49 studies were identified as being relevant to the topic of interest, and among these studies, there were 31 different databases and 24 different machine learning methods.
Conclusions: The results provide a better understanding of how smartphone data are collected for predicting different diseases and what kinds of machine learning methods are used on these data. Similarly, publicly available databases having smartphone-based data that can be used for the diagnosis of various diseases have been presented. Our screening method could be used or improved in future studies, and our findings could be used as a reference to conduct similar studies, experiments, or statistical analyses.
(©Research Dawadi, Mai Inoue, Jie Ting Tay, Agustin Martin-Morales, Thien Vu, Michihiro Araki. Originally published in JMIR AI (https://ai.jmir.org), 25.03.2025.)