Key result
Deep learning using pharyngeal images detects hypertension with ~0.92 AUROC, outperforming demographics alone.
Why the study?
Early hypertension detection via visual images without physical interaction or devices could enhance telemedicine, and pharyngeal images containing vascular morphology may be useful for this purpose.
Does a deep learning-based algorithm using pharyngeal images improve the detection of hypertension compared to demographic information alone in primary care patients?
Cross-Sectional (n=7,710)
Yes
Does a deep learning-based algorithm using pharyngeal images improve the detection of hypertension compared to demographic information alone in primary care patients?
A deep learning algorithm using pharyngeal images can accurately detect hypertension, offering a potential contactless screening tool for telemedicine.
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.
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Yoshihara et al. (2024) conducted a cross-sectional in Hypertension (n=7,710). Deep learning-based algorithm using pharyngeal images and demographic information vs. Baseline prediction model incorporating only demographic information was evaluated on Detection of hypertension (AUROC) (AUROC 0.922, 95% CI 0.904 to 0.940). A deep learning-based algorithm using pharyngeal images and demographic information detected hypertension with an AUROC of 0.922, significantly improving upon a demographics-only model.
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