Detection of hypertension from pharyngeal images using deep learning algorithm in primary care settings in Japan.

IF 4.1 Q1 HEALTH CARE SCIENCES & SERVICES BMJ Health & Care Informatics Pub Date : 2024-10-23 DOI:10.1136/bmjhci-2023-100824
Hiroshi Yoshihara, Yusuke Tsugawa, Memori Fukuda, Sho Okiyama, Takeo Nakayama
{"title":"Detection of hypertension from pharyngeal images using deep learning algorithm in primary care settings in Japan.","authors":"Hiroshi Yoshihara, Yusuke Tsugawa, Memori Fukuda, Sho Okiyama, Takeo Nakayama","doi":"10.1136/bmjhci-2023-100824","DOIUrl":null,"url":null,"abstract":"<p><strong>Background: </strong>The early detection of hypertension using simple visual images in a way that does not require physical interaction or additional devices may improve quality of care in the era of telemedicine. Pharyngeal images include vascular morphological information and may therefore be useful for identifying hypertension.</p><p><strong>Objectives: </strong>This study sought to develop a deep learning-based artificial intelligence algorithm for identifying hypertension from pharyngeal images.</p><p><strong>Methods: </strong>We conducted a secondary analysis of data from a clinical trial, in which demographic information, vital signs and pharyngeal images were obtained from patients with influenza-like symptoms in multiple primary care clinics in Japan. A deep learning-based algorithm that included a multi-instance convolutional neural network was trained to detect hypertension from pharyngeal images and demographic information. The classification performance was measured by area under the receiver operating characteristic curve. Importance heatmaps of the convolutional neural network were also examined to interpret the algorithm.</p><p><strong>Results: </strong>This study included 7710 patients from 64 clinics. The training dataset comprised 6171 patients from 51 clinics (460 positive cases), and the test dataset comprised 1539 patients from 13 clinics (130 positive cases). Our algorithm achieved an area under the receiver operating characteristic curve of 0.922 (95% CI, 0.904 to 0.940), significantly improving over the baseline prediction model incorporating only demographic information, which scored 0.887 (95% CI, 0.862 to 0.911). Our algorithm had consistent classification performance across all age and sex subgroups. Importance heatmaps revealed that the algorithm focused on the posterior pharyngeal wall area, where blood vessels are mainly located.</p><p><strong>Conclusions: </strong>The results indicate that a deep learning-based algorithm can detect hypertension with high accuracy using pharyngeal images.</p>","PeriodicalId":9050,"journal":{"name":"BMJ Health & Care Informatics","volume":"31 1","pages":""},"PeriodicalIF":4.1000,"publicationDate":"2024-10-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11499848/pdf/","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"BMJ Health & Care Informatics","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1136/bmjhci-2023-100824","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"HEALTH CARE SCIENCES & SERVICES","Score":null,"Total":0}
引用次数: 0

Abstract

Background: The early detection of hypertension using simple visual images in a way that does not require physical interaction or additional devices may improve quality of care in the era of telemedicine. Pharyngeal images include vascular morphological information and may therefore be useful for identifying hypertension.

Objectives: This study sought to develop a deep learning-based artificial intelligence algorithm for identifying hypertension from pharyngeal images.

Methods: We conducted a secondary analysis of data from a clinical trial, in which demographic information, vital signs and pharyngeal images were obtained from patients with influenza-like symptoms in multiple primary care clinics in Japan. A deep learning-based algorithm that included a multi-instance convolutional neural network was trained to detect hypertension from pharyngeal images and demographic information. The classification performance was measured by area under the receiver operating characteristic curve. Importance heatmaps of the convolutional neural network were also examined to interpret the algorithm.

Results: This study included 7710 patients from 64 clinics. The training dataset comprised 6171 patients from 51 clinics (460 positive cases), and the test dataset comprised 1539 patients from 13 clinics (130 positive cases). Our algorithm achieved an area under the receiver operating characteristic curve of 0.922 (95% CI, 0.904 to 0.940), significantly improving over the baseline prediction model incorporating only demographic information, which scored 0.887 (95% CI, 0.862 to 0.911). Our algorithm had consistent classification performance across all age and sex subgroups. Importance heatmaps revealed that the algorithm focused on the posterior pharyngeal wall area, where blood vessels are mainly located.

Conclusions: The results indicate that a deep learning-based algorithm can detect hypertension with high accuracy using pharyngeal images.

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
日本基层医疗机构利用深度学习算法从咽部图像检测高血压。
背景:在远程医疗时代,利用简单的视觉图像进行高血压的早期检测,无需身体互动或额外设备,可提高医疗质量。咽部图像包括血管形态信息,因此可能有助于识别高血压:本研究试图开发一种基于深度学习的人工智能算法,用于从咽部图像中识别高血压:我们对一项临床试验的数据进行了二次分析,该试验从日本多家初级保健诊所的流感样症状患者那里获得了人口统计学信息、生命体征和咽部图像。我们训练了一种基于深度学习的算法,其中包括一个多实例卷积神经网络,用于从咽部图像和人口统计学信息中检测高血压。分类性能通过接收者工作特征曲线下面积进行测量。此外,还研究了卷积神经网络的重要性热图,以解释该算法:这项研究包括来自 64 家诊所的 7710 名患者。训练数据集包括 51 家诊所的 6171 名患者(460 个阳性病例),测试数据集包括 13 家诊所的 1539 名患者(130 个阳性病例)。我们的算法的接收者操作特征曲线下面积为 0.922(95% CI,0.904 至 0.940),明显优于仅包含人口统计学信息的基线预测模型,后者的得分为 0.887(95% CI,0.862 至 0.911)。在所有年龄和性别分组中,我们的算法都具有一致的分类性能。重要性热图显示,该算法侧重于咽后壁区域,而血管主要位于该区域:结果表明,基于深度学习的算法可以利用咽部图像高精度地检测出高血压。
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
CiteScore
6.10
自引率
4.90%
发文量
40
审稿时长
18 weeks
期刊最新文献
Scaling equitable artificial intelligence in healthcare with machine learning operations. Understanding prescribing errors for system optimisation: the technology-related error mechanism classification. Detection of hypertension from pharyngeal images using deep learning algorithm in primary care settings in Japan. PubMed captures more fine-grained bibliographic data on scientific commentary than Web of Science: a comparative analysis. Method to apply temporal graph analysis on electronic patient record data to explore healthcare professional-patient interaction intensity: a cohort study.
×
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:481959085
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