Department of Health Informatics, Kyoto University School of Public Health, Kyoto, Japan
Aillis, Inc, Tokyo, Japan.
BMJ Health Care Inform. 2024 Oct 23;31(1):e100824. doi: 10.1136/bmjhci-2023-100824.
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.
This study sought to develop a deep learning-based artificial intelligence algorithm for identifying hypertension from pharyngeal images.
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.
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.
The results indicate that a deep learning-based algorithm can detect hypertension with high accuracy using pharyngeal images.
通过无需身体接触或额外设备的简单视觉图像来早期检测高血压，可能会提高远程医疗时代的护理质量。咽图像包含血管形态学信息，因此可能有助于识别高血压。
本研究旨在开发一种基于深度学习的人工智能算法，用于从咽图像中识别高血压。
我们对一项临床试验中的数据进行了二次分析，该试验在日本的多个初级保健诊所中对流感样症状患者进行了人口统计学信息、生命体征和咽图像的采集。我们训练了一种基于深度学习的算法，该算法包括一个多实例卷积神经网络，用于从咽图像和人口统计学信息中检测高血压。通过接受者操作特征曲线下的面积来衡量分类性能。还检查了卷积神经网络的重要性热图，以解释算法。
本研究纳入了来自 64 家诊所的 7710 名患者。训练数据集包括来自 51 家诊所的 6171 名患者（460 例阳性病例），测试数据集包括来自 13 家诊所的 1539 名患者（130 例阳性病例）。我们的算法在接受者操作特征曲线下的面积为 0.922（95%置信区间，0.904 至 0.940），明显优于仅纳入人口统计学信息的基线预测模型（0.887，95%置信区间，0.862 至 0.911）。我们的算法在所有年龄和性别亚组中的分类性能均一致。重要性热图显示，该算法主要集中在后咽壁区域，那里主要分布着血管。
研究结果表明，基于深度学习的算法可以使用咽图像准确地检测高血压。
