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

IF 2 JMIR AI Pub Date : 2025-03-25 DOI:10.2196/59094
Research Dawadi, Mai Inoue, Jie Ting Tay, Agustin Martin-Morales, Thien Vu, Michihiro Araki
{"title":"Disease Prediction Using Machine Learning on Smartphone-Based Eye, Skin, and Voice Data: Scoping Review.","authors":"Research Dawadi, Mai Inoue, Jie Ting Tay, Agustin Martin-Morales, Thien Vu, Michihiro Araki","doi":"10.2196/59094","DOIUrl":null,"url":null,"abstract":"<p><strong>Background: </strong>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.</p><p><strong>Objective: </strong>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.</p><p><strong>Methods: </strong>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.</p><p><strong>Results: </strong>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.</p><p><strong>Conclusions: </strong>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.</p>","PeriodicalId":73551,"journal":{"name":"JMIR AI","volume":"4 ","pages":"e59094"},"PeriodicalIF":2.0000,"publicationDate":"2025-03-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11979540/pdf/","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"JMIR AI","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.2196/59094","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

Abstract

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.

Abstract Image

Abstract Image

Abstract Image

查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
使用基于智能手机的眼睛、皮肤和语音数据的机器学习进行疾病预测:范围审查。
背景:将机器学习方法应用于智能手机等无处不在的设备产生的数据,为提高医疗保健和诊断质量提供了机会。智能手机是方便收集数据、快速提供诊断反馈和提出改善健康干预措施的理想工具。目的:我们回顾了现有文献,收集了使用智能手机衍生数据的机器学习模型来预测和诊断健康异常的研究。我们将这些研究分为两类,一类是通过实验来检索数据和预测疾病,使用机器学习模型;另一类是在公共数据库上使用机器学习模型。数据库、实验和机器学习模型的细节旨在帮助在医疗保健领域的机器学习和人工智能领域工作的研究人员。研究人员可以使用这些信息来设计他们的实验或确定他们可以分析的数据库。方法:综合检索PubMed和IEEE Xplore数据库,采用内部关键词筛选方法,根据文章标题和摘要的内容对文章进行筛选。随后,根据提取机器学习模型数据的方式(即使用公开可用的数据库或通过实验),选择和分析与声音、皮肤和眼睛三个领域相关的研究。还注意到每个研究中使用的机器学习方法。结果:共有49项研究被确定为与感兴趣的主题相关,其中有31个不同的数据库和24种不同的机器学习方法。结论:这些结果更好地理解了如何收集智能手机数据来预测不同的疾病,以及在这些数据上使用了什么样的机器学习方法。同样,已经提出了具有基于智能手机的数据的公开数据库,可用于诊断各种疾病。我们的筛选方法可以在未来的研究中使用或改进,我们的发现可以作为类似研究、实验或统计分析的参考。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 去求助
来源期刊
自引率
0.00%
发文量
0
期刊最新文献
Artificial Intelligence as a Catalyst for Value-Based Health Insurance in the United States: Narrative Review and Policy Perspective. Deep Learning for Age Estimation and Sex Prediction Using Mandibular-Cropped Cephalometric Images: Comparative Model Development and Validation Study. Perspectives on How Sociology Can Advance Theorizing About Human-Chatbot Interaction and Developing Chatbots for Social Good. Large Language Model-Based Chatbots and Agentic AI for Mental Health Counseling: Systematic Review of Methodologies, Evaluation Frameworks, and Ethical Safeguards. Correction: Real-World Evidence Synthesis of Digital Scribes Using Ambient Listening and Generative Artificial Intelligence for Clinician Documentation Workflows: Rapid Review.
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:604180095
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1