In this study, we developed a novel heart rate (HR) monitoring approach in which we measure the pressure variance of the surface of the ear canal. A scissor-shaped apparatus equipped with a piezoelectric film sensor and a hardware circuit module was designed for high wearability and to obtain stable measurement. In the proposed device, the film sensor converts in-ear pulse waves (EPW) into electrical current, and the circuit module enhances the EPW and suppresses noise. A real-time algorithm embedded in the circuit module performs morphological conversions to make the EPW more distinct and knowledge-based rules are used to detect EPW peaks. In a clinical experiment conducted using a reference electrocardiogram (ECG) device, EPW and ECG were concurrently recorded from 58 healthy subjects. The EPW intervals between successive peaks and their corresponding ECG intervals were then compared to each other. Promising results were obtained from the samples, specifically, a sensitivity of 97.25%, positive predictive value of 97.17%, and mean absolute difference of 0.62. Thus, highly accurate HR was obtained from in-ear pressure variance. Consequently, we believe that our proposed approach could be used to monitor vital signs and also utilized in diverse applications in the near future.

Determining the exact positional relationship between mandibular third molar (M3) and inferior alveolar nerve (IAN) is important for surgical extractions. Panoramic radiography is the most common dental imaging test. The purposes of this study were to develop an artificial intelligence (AI) model to determine two positional relationships (true contact and bucco-lingual position) between M3 and IAN when they were overlapped in panoramic radiographs and compare its performance with that of oral and maxillofacial surgery (OMFS) specialists. A total of 571 panoramic images of M3 from 394 patients was used for this study. Among the images, 202 were classified as true contact, 246 as intimate, 61 as IAN buccal position, and 62 as IAN lingual position. A deep convolutional neural network model with ResNet-50 architecture was trained for each task. We randomly split the dataset into 75% for training and validation and 25% for testing. Model performance was superior in bucco-lingual position determination (accuracy 0.76, precision 0.83, recall 0.67, and F1 score 0.73) to true contact position determination (accuracy 0.63, precision 0.62, recall 0.63, and F1 score 0.61). AI exhibited much higher accuracy in both position determinations compared to OMFS specialists. In determining true contact position, OMFS specialists demonstrated an accuracy of 52.68% to 69.64%, while the AI showed an accuracy of 72.32%. In determining bucco-lingual position, OMFS specialists showed an accuracy of 32.26% to 48.39%, and the AI showed an accuracy of 80.65%. Moreover, Cohen’s kappa exhibited a substantial level of agreement for the AI (0.61) and poor agreements for OMFS specialists in bucco-lingual position determination. Determining the position relationship between M3 and IAN is possible using AI, especially in bucco-lingual positioning. The model could be used to support clinicians in the decision-making process for M3 treatment.

Temporomandibular joint osteoarthritis (TMJ OA) is a degenerative condition of the TMJ led by a pathological tissue response of the joint under mechanical loading. It is characterized by the progressive destruction of the internal surfaces of the joint, which can result in debilitating pain and joint noise. Panoramic imaging can be used as a basic screening tool with thorough clinical examination in diagnosing TMJ OA. This paper proposes an algorithm that can extract the condylar region and determine its abnormality by using convolutional neural networks (CNNs) and Faster region-based CNNs (R-CNNs). Panoramic images are collected retrospectively and 1000 images are classified into three categories—normal, abnormal, and unreadable—by a dentist or orofacial pain specialist. Labels indicating whether the condyle is detected and its location enabled more clearly recognizable panoramic images. The uneven proportion of normal to abnormal data is adjusted by duplicating and rotating the images. An R-CNN model and a Visual Geometry Group-16 (VGG16) model are used for learning and condyle discrimination, respectively. To prevent overfitting, the images are rotated ±10° and shifted by 10%. The average precision of condyle detection using an R-CNN at intersection over union (IoU) >0.5 is 99.4% (right side) and 100% (left side). The sensitivity, specificity, and accuracy of the TMJ OA classification algorithm using a CNN are 0.54, 0.94, and 0.84, respectively. The findings demonstrate that classifying panoramic images through CNNs is possible. It is expected that artificial intelligence will be more actively applied to analyze panoramic X-ray images in the future.

Dental panoramic radiography (DPR) is a method commonly used in dentistry for patient diagnosis. This study presents a new technique that combines a regional convolutional neural network (RCNN), Single Shot Multibox Detector, and heuristic methods to detect and number the teeth and implants with only fixtures in a DPR image. This technology is highly significant in providing statistical information and personal identification based on DPR and separating the images of individual teeth, which serve as basic data for various DPR-based AI algorithms. As a result, the mAP(@IOU = 0.5) of the tooth, implant fixture, and crown detection using the RCNN algorithm were obtained at rates of 96.7%, 45.1%, and 60.9%, respectively. Further, the sensitivity, specificity, and accuracy of the tooth numbering algorithm using a convolutional neural network and heuristics were 84.2%, 75.5%, and 84.5%, respectively. Techniques to analyze DPR images, including implants and bridges, were developed, enabling the possibility of applying AI to orthodontic or implant DPR images of patients.

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The aim of this study was to predict chronic diseases in individual patients using a character-recurrent neural network (Char-RNN), which is a deep learning model that treats data in each class as a word when a large portion of its input values is missing. An advantage of Char-RNN is that it does not require any additional imputation method because it implicitly infers missing values considering the relationship with nearby data points. We applied Char-RNN to classify cases in the Korea National Health and Nutrition Examination Survey (KNHANES) VI as normal status and five chronic diseases: hypertension, stroke, angina pectoris, myocardial infarction, and diabetes mellitus. We also employed a multilayer perceptron network for the same task for comparison. The results show higher accuracy for Char-RNN than for the conventional multilayer perceptron model. Char-RNN showed remarkable performance in finding patients with hypertension and stroke. The present study utilized the KNHANES VI data to demonstrate a practical approach to predicting and managing chronic diseases with partially observed information.

Objective: The objectives of this study are (1) to establish a ubiquitous healthcare (u-healthcare) center for those who wish to use u-healthcare, allowing them to experience the service, and (2) to evaluate the users' awareness and expectations of the service based on their overall assessment. Materials and Methods: To establish the u-healthcare center, a kiosk, devices for health checkup, a body-type examination system, and a physical fitness assessment system were installed. Also, a u-healthcare Web site was developed. A survey was conducted on 280 individuals who visited the u-healthcare center and used the service, to determine (1) individual awareness of u-healthcare before using the service and their change of perception after use, (2) factors that affect the use of u-healthcare, and (3) the effects of disease awareness on exercise habits. Results: Only 25.4% of the participants were aware of u-healthcare, and only 36% who saw the u-healthcare center recognized that it was where the u-healthcare service was provided. The group of individuals who were willing to use the u-healthcare showed statistically significant differences in their satisfaction with the overall environment of the center, as well as the specificity of the descriptions, examination results, kindness of the staff, and their responses. Additionally, the group of individuals who were diagnosed with chronic diseases and the group who were not showed statistically significant differences in the number of days on which they exercised lightly or took a walk. Conclusions: To promote the usage of u-healthcare service, the understanding of the service and the credibility of examination results need to be increased by sharing successful cases. Furthermore, to expand the use of the system that allows a person to regularly check his or her state of health, a lifelong periodical management system linked with another medical welfare program will be needed.