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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.
Introduction
Hypertension is a major global disease burden, with more than one billion people—or approximately 40% of adults aged 25 and above—diagnosed with hypertension worldwide.1 If it is not appropriately diagnosed and treated, hypertension can lead to major complications such as myocardial infarction, stroke and chronic kidney disease.2–4 However, given that hypertension is often asymptomatic, it is often unidentified and undiagnosed. Even when it is appropriately diagnosed, many patients are untreated or have insufficient control of their blood pressure.1 Complications of hypertension account for approximately 10 million deaths every year worldwide1; this issue is particularly salient in low- and middle-income countries where there is limited access to healthcare.1
Underdiagnosis is a major issue in hypertension because untreated patients are at increased risk of both all-cause and other disease-related mortality.5 Globally, the proportion of people with undiagnosed hypertension is estimated to be 41% in women and 51% in men.6 This large discrepancy between the prevalence and awareness of hypertension is mainly caused by a lack of access to healthcare and/or insufficient resources for diagnosing hypertension, such as blood pressure monitoring devices.7–9 A US survey reported that only 55% of patients with hypertension use blood pressure monitoring devices at home10; this number is estimated to be far lower in the general population (including those individuals without diagnosed hypertension). Easier and more accessible diagnosis without the use of medical devices can be an effective solution to the underdiagnosis of hypertension. Given that the number of mobile phone users is 7.3 billion, which is 91.5% of the world’s population as of 2022,11 we hypothesised that a new diagnostic technique for hypertension that uses the cameras on mobile phones has the potential to reduce the underdiagnosis of hypertension and ultimately lower hypertension-related morbidity and mortality.
Hypertension causes vascular damage and remodelling.12 13 Previous studies on the use of retinal fundus images to predict hypertension14 and other diseases15 have reported that geometrical changes in blood vessels are important for diagnosis. Pharyngeal images contain vascular information; we hypothesised that vascular changes caused by hypertension may be visible on such images. With the possibility of detecting hypertension through images, smartphone-based diagnosis can be cheaper, faster and more accessible compared with the conventional sphygmomanometer-based method. It also has the potential to improve the quality of telemedicine by addressing the current difficulties of diagnosing hypertension without additional medical devices. In this study, we therefore developed and investigated a deep learning-based algorithm for the accurate detection of hypertension using pharyngeal images, with a view to its future application in telemedicine.
Discussion
Through this study, we have discovered both the high accuracy of our algorithm in diagnosing hypertension and the contribution of pharyngeal images in improving the accuracy of hypertension diagnosis. Although studies using pharyngeal images to diagnose influenza26 and identify sex27 have already been reported, to the best of our knowledge, this is the first study to demonstrate the utility of pharyngeal images in diagnosing hypertension. It has also been reported that chest radiographs28 and retinal images15 can be used to diagnose lifestyle-related diseases. However, pharyngeal images can be captured with much lower invasiveness compared with these images and without the need for special devices, making them highly compatible with digital therapeutics and telemedicine. Our findings suggest the potential for the future development of contactless diagnostics incorporating pharyngeal images would be immensely helpful.
Based on the results of our analysis using Grad-CAM heatmaps, we hypothesised that the deep learning model may diagnose hypertension by identifying specific blood vessel patterns (eg, stenosis and tortuosity) and arteriosclerosis caused by hypertension. Given that the pharynx is one of the few body parts in which blood vessels can be observed from the outside, images of the pharynx may provide similar information to the retinal images reported in a previous study.14
Our study was built on prior research that sought to diagnose hypertension without using blood pressure cuffs. Recent studies have used physiological data, such as photoplethysmography (PPG) or electrocardiography, to detect hypertension. Liang et al converted PPG signals into a scalogram using continuous wavelet transform and then predicted hypertension from the scalogram using a CNN.29 The authors reported an F1-score of 92.55% for binary classification between normotension and hypertension. Similarly, other studies have explored the possibility of detecting hypertension from PPG signals using the open PPG-BP dataset,30 which contains only 219 patients.31–33 While informative, these studies are limited by their small dataset sizes, making evaluation metrics difficult to generalise. In contrast, our approach to diagnosing hypertension using pharyngeal images was validated using a relatively large dataset.
Limitations
The present study has some limitations. First, a certain degree of misclassification may occur in the definition of hypertension. To address the various issues associated with blood pressure measurements, hypertension was defined based on history and medication information in the main analysis. However, it should be noted that we cannot identify people with undiagnosed hypertension using this method. Additionally, if disease history or medication history is not available at the clinic, it becomes necessary to obtain this information from the patients through verbal communication, which may be subject to the uncertainty of the patients’ recollections. To address this issue, we conducted a sensitivity analysis to define hypertension using the measured blood pressure data and confirmed that our findings were not sensitive to how we define and identify hypertensive individuals. Second, potential biases can be considered in the patient population of this study. The patients in this study were recruited from primary care clinics in Japan, and it is likely that they share a high degree of ethnic homogeneity. Also, the subjects of this study were patients exhibiting influenza-like symptoms, and it is possible that several background factors differ from those of the general population. Therefore, the external validity of the study’s findings may be limited.
Conclusions
We developed a deep learning-based algorithm for diagnosing hypertension from pharyngeal images and basic demographic information, demonstrating the utility of pharyngeal images in the diagnosis of hypertension. Although we used a specially designed medical camera in this study, our findings further suggest the feasibility of developing an algorithm that can diagnose hypertension using pharyngeal images captured by smartphones in the future, thus addressing one of the major limitations of telemedicine: the difficulty of collecting data that allow a diagnosis to be made. Such technology will be particularly useful during pandemic situations, such as the COVID-19 pandemic, when diagnoses need to be made without physical interactions between patients and healthcare providers.
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