Detection of hypertension from pharyngeal images using deep learning algorithm in primary care settings in Japan
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 ide...
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| Format: | Article |
| Language: | English |
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BMJ Publishing Group
2024-09-01
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| Series: | BMJ Health & Care Informatics |
| Online Access: | https://informatics.bmj.com/content/31/1/e100824.full |
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| author | Takeo Nakayama Yusuke Tsugawa Hiroshi Yoshihara Memori Fukuda Sho Okiyama |
| author_facet | Takeo Nakayama Yusuke Tsugawa Hiroshi Yoshihara Memori Fukuda Sho Okiyama |
| author_sort | Takeo Nakayama |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-c89f7c1a2a2c4908b5c00d765d16a898 |
| institution | OA Journals |
| issn | 2632-1009 |
| language | English |
| publishDate | 2024-09-01 |
| publisher | BMJ Publishing Group |
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| series | BMJ Health & Care Informatics |
| spelling | doaj-art-c89f7c1a2a2c4908b5c00d765d16a8982025-08-20T02:27:19ZengBMJ Publishing GroupBMJ Health & Care Informatics2632-10092024-09-0131110.1136/bmjhci-2023-100824Detection of hypertension from pharyngeal images using deep learning algorithm in primary care settings in JapanTakeo Nakayama0Yusuke Tsugawa1Hiroshi Yoshihara2Memori Fukuda3Sho Okiyama4Department of Health Informatics, Kyoto University School of Public Health, Kyoto, Japan11 Department of Health Policy and Management, Fielding School of Public Health, University of California, Los Angeles, California, USADepartment of Health Informatics, Kyoto University School of Public Health, Kyoto, JapanAillis, Inc, Tokyo, JapanAillis, Inc, Tokyo, JapanBackground 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.https://informatics.bmj.com/content/31/1/e100824.full |
| spellingShingle | Takeo Nakayama Yusuke Tsugawa Hiroshi Yoshihara Memori Fukuda Sho Okiyama Detection of hypertension from pharyngeal images using deep learning algorithm in primary care settings in Japan BMJ Health & Care Informatics |
| title | Detection of hypertension from pharyngeal images using deep learning algorithm in primary care settings in Japan |
| title_full | Detection of hypertension from pharyngeal images using deep learning algorithm in primary care settings in Japan |
| title_fullStr | Detection of hypertension from pharyngeal images using deep learning algorithm in primary care settings in Japan |
| title_full_unstemmed | Detection of hypertension from pharyngeal images using deep learning algorithm in primary care settings in Japan |
| title_short | Detection of hypertension from pharyngeal images using deep learning algorithm in primary care settings in Japan |
| title_sort | detection of hypertension from pharyngeal images using deep learning algorithm in primary care settings in japan |
| url | https://informatics.bmj.com/content/31/1/e100824.full |
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