Predicting Obstructive Sleep Apnea Based on Computed Tomography Scan Using Deep Learning Models.

RATIONALE: The incidence of clinically undiagnosed obstructive sleep apnea (OSA) is high among the general population due to limited access to polysomnography. Computed tomography (CT) of craniofacial regions obtained for other purposes can be beneficial in predicting OSA and its severity. OBJECTIVES: To predict OSA and its severity based on paranasal CT using a 3-dimensional deep learning algorithm. METHODS: One internal dataset (n=798) and two external datasets (n=135 and 85) were used in this study. In the internal dataset, 92 normal, 159 mild, 201 moderate, and 346 severe OSA participants were enrolled to derive the deep learning model. A multimodal deep learning model was elicited from the connection between a 3-dimensional convolutional neural network (CNN)-based part treating unstructured data (CT images) and a multi-layer perceptron (MLP)-based part treating structured data (age, sex, and body mass index) to predict OSA and its severity. MEASUREMENTS AND MAIN RESULTS: In four-class classification for predicting the severity of OSA, the AirwayNet-MM-H model (multimodal model with airway-highlighting preprocessing algorithm) showed an average accuracy of 87.6% (95% confidence interval [CI] 86.8-88.6) in the internal dataset and 84.0% (95% CI 83.0-85.1) and 86.3% (95% CI 85.3-87.3) in the two external datasets, respectively. In the two-class classification for predicting significant OSA (moderate to severe OSA), The area under the receiver operating characteristics (AUROC), accuracy, sensitivity, specificity, and F1 score were 0.910 (95% CI 0.899-0.922), 91.0% (95% CI 90.1-91.9), 89.9% (95% CI 88.8-90.9), 93.5% (95% CI 92.7-94.3), and 93.2% (95% CI 92.5-93.9), respectively, in the internal dataset. Furthermore, the diagnostic performance of the Airway Net-MM-H model outperformed that of the other six state-of-the-art deep learning models in terms of accuracy for both four- and two-class classifications and AUROC for two-class classification (p<0.001). CONCLUSIONS: A novel deep learning model, including a multimodal deep learning model and an airway-highlighting preprocessing algorithm from CT images obtained for other purposes, can provide significantly precise outcomes for OSA diagnosis.

Deep Learning-Based Diagnostic System for Velopharyngeal Insufficiency Based on Videofluoroscopy in Patients With Repaired Cleft Palates.

Velopharyngeal insufficiency (VPI), which is the incomplete closure of the velopharyngeal valve during speech, is a typical poor outcome that should be evaluated after cleft palate repair. The interpretation of VPI considering both imaging analysis and perceptual evaluation is essential for further management. The authors retrospectively reviewed patients with repaired cleft palates who underwent assessment for velopharyngeal function, including both videofluoroscopic imaging and perceptual speech evaluation. The final diagnosis of VPI was made by plastic surgeons based on both assessment modalities. Deep learning techniques were applied for the diagnosis of VPI and compared with the human experts' diagnostic results of videofluoroscopic imaging. In addition, the results of the deep learning techniques were compared with a speech pathologist's diagnosis of perceptual evaluation to assess consistency with clinical symptoms. A total of 714 cases from January 2010 to June 2019 were reviewed. Six deep learning algorithms (VGGNet, ResNet, Xception, ResNext, DenseNet, and SENet) were trained using the obtained dataset. The area under the receiver operating characteristic curve of the algorithms ranged between 0.8758 and 0.9468 in the hold-out method and between 0.7992 and 0.8574 in the 5-fold cross-validation. Our findings demonstrated the deep learning algorithms performed comparable to experienced plastic surgeons in the diagnosis of VPI based on videofluoroscopic velopharyngeal imaging.

Augmented Reality-Based Visual Cue for Guiding Central Catheter Insertion in Pediatric Oncologic Patients.

BACKGROUND: Pediatric hemato-oncologic patients require central catheters for chemotherapy, and the junction of the superior vena cava and right atrium is considered the ideal location for catheter tips. Skin landmarks or fluoroscopic supports have been applied to identify the cavoatrial junction; however, none has been recognized as the gold standard. Therefore, we aim to develop a safe and accurate technique using augmented reality technology for the location of the cavoatrial junction in pediatric hemato-oncologic patients. METHODS: Fifteen oncology patients who underwent chest computed tomography were enrolled for Hickman catheter or chemoport insertion. With the aid of augmented reality technology, three-dimensional models of the internal jugular veins, external jugular veins, subclavian veins, superior vena cava, and right atrium were constructed. On inserting the central vein catheters, the cavoatrial junction identified using the three-dimensional models were marked on the body surface, the tip was positioned at the corresponding location, and the actual insertion location was confirmed using a portable x-ray machine. The proposed method was evaluated by comparing the distance from the cavoatrial junction to the augmented reality location with that to the conventional location on x-ray. RESULTS: The mean distance between the cavoatrial junction and augmented reality location on x-ray was 1.2 cm, which was significantly shorter than that between the cavoatrial junction and conventional location (1.9 cm; P = 0.027). CONCLUSIONS: Central catheter insertion using augmented reality technology is more safe and accurate than that using conventional methods and can be performed at no additional cost in oncology patients.

Effects of smartphone mirroring-based telepresence exercise on body composition and physical function in obese older women.

AIMS: This study aimed to develop a smartphone mirroring-based telepresence exercise program that can be performed at home while allowing for real-time feedback by instructors. METHODS: For this randomized controlled trial, 29 obese older women aged 66-87 years with ≥ 30% body fat were recruited at a senior citizen center. The intervention group was provided with the smartphone mirroring-based telepresence exercise program, in which participants exercised in their homes for 20-40 min three times a week for 12 weeks. Participants in the control group performed the same exercise program at the senior citizen center. Body composition and functional abilities were measured before and after the program. RESULTS: Women in the intervention group showed a decrease in their body fat percentage (P = 0.026) and an increase in grip strength (P = 0.008). In the control group, women demonstrated a decrease in their weight (P = 0.006) and body fat percentage (P = 0.001) and an increase in skeletal muscle (P = 0.044) and grip strength (P = 0.006). CONCLUSION: Smartphone mirroring-based telepresence exercises at home lower body fat percentage and increase muscle strength similar to traditional group exercises. They present an innovative way for obese older women to improve and maintain their health. TRIAL REGISTRATION: Clinical Research Information Service KCT0006147.

Enhancement in Performance of Septic Shock Prediction Using National Early Warning Score, Initial Triage Information, and Machine Learning Analysis.

BACKGROUND: Several studies reported that the National Early Warning Score (NEWS) has shown superiority over other screening tools in discriminating emergency department (ED) patients who are likely to progress to septic shock. OBJECTIVES: To improve the performance of the NEWS for septic shock prediction by adding variables collected during ED triage, and to implement a machine-learning algorithm. METHODS: The study population comprised adult ED patients with suspected infection. To detect septic shock within 24 h after ED arrival, the Sepsis-3 clinical criteria and nine variables were used: NEWS, age, gender, systolic blood pressure, diastolic blood pressure, pulse rate, respiratory rate, body temperature, and oxygen saturation. The model was developed using logistic regression (LR), extreme gradient boosting (XGB), and artificial neural network (ANN) algorithms. The evaluations were performed using an area under the receiver operating characteristic curve (AUROC), Hosmer-Lemeshow test, and net reclassification index (NRI). RESULTS: Overall, 41,687 patients were enrolled. The AUROC of the model with NEWS, age, gender, and the six vital signs (0.835-0.845) was better than that of the baseline model (0.804). The XGB model (AUROC 0.845) was the most accurate, compared with LR (0.844) and ANN (0.835). The LR and XGB models were well calibrated; however, the ANN showed poor calibration power. The LR and XGB models showed better reclassification than the baseline model with positive NRI. CONCLUSION: The discrimination power of the model for screening septic shock using NEWS, age, gender, and the six vital signs collected at ED triage outperformed the baseline NEWS model.

Investigating the Need for Point-of-Care Robots to Support Teleconsultation.

Background: Development of a point-of-care (POC) consultation system based on telepresence robots is needed to enable effective decision-making by medical staff at care sites.Introduction: This study aimed to identify essential features and functional requirements of teleconsultation robot systems and predict potential administrative and clinical issues.Materials and Methods: Surveys were conducted with 90 health care professionals and additional focus group interviews with 4 physicians and 5 nurses. The questionnaire for the surveys was developed by the authors. Survey results were analyzed using descriptive statistics, and content analysis was used to extract themes from the unabridged transcripts of focus group interviews.Results: The most desired functionalities were related to patient evaluation and facilitation of communication, including measuring vital signs, and medical record sharing and delivery. Nurses and physicians reported different needs for human-robot interactions. Nurses valued robotic functions such as voice command, automatic camera movement, voice recognition with contextual perception, and recognition of nonverbal signals. The thematic analysis of the interviews yielded four themes: major functions, usability, expected effects, and potential issues. The results indicated that robots should primarily be employed to support communication between medical professionals. The major expected effects included prevention of treatment delays and decision-making assistance. Participants believed that teleconsultation robots would be helpful, but had concerns, including anxiety about the robots and judgment errors.Conclusions: Using robots in health care institutions may support effective communication among medical staff, thus contributing to health care improvement. In the future, an actual POC robot system will be developed and its effectiveness evaluated.

Development of an aperture-type radiation regulator for shielding against secondary radiation from x-ray tubes and collimators in computed tomography.

During computed tomography (CT) scans, radiation scatters in all directions, increasing radiation exposure. In this study, an aperture-type radiation regulator was developed to provide shielding against secondary radiation from the x-ray tube and collimator in CT. To evaluate the usefulness of the developed aperture-type radiation regulator, (1) spatial dose distribution within the CT room was measured, (2) dose intensity at 1 m from the isocenter was compared, (3) absorbed dose in the nearby organs was evaluated using a human equivalent phantom, and (4) noise, CNR, and SNR were compared for assessment of image quality. The results showed that the developed aperture-type radiation regulator reduced the intensity of secondary radiation by approximately 25% in front of the gantry and 15% to the rear of the gantry. The maximum dose distribution on 10 µGy was reduced by approximately 18% in front of the gantry and 12% in the rear. In addition, when the neck and head were scanned, the absorbed dose in the chest decreased by 25% and 40%, respectively, and noise was reduced by 3.3%-4.5% for different phantoms. Evaluation of abdominal CT images showed 18% noise reduction, with 27% and 28% increases in the signal-to-noise and contrast-to-noise ratios, respectively. These results confirmed that the proposed aperture-type radiation regulator can reduce radiation exposure without affecting primary radiation that creates medical images. The results also confirmed that the radiation regulator effectively improves the quality of medical images.

A novel summary report of colonoscopy: timeline visualization providing meaningful colonoscopy video information.

PURPOSE: The colonoscopy adenoma detection rate depends largely on physician experience and skill, and overlooked colorectal adenomas could develop into cancer. This study assessed a system that detects polyps and summarizes meaningful information from colonoscopy videos. METHODS: One hundred thirteen consecutive patients had colonoscopy videos prospectively recorded at the Seoul National University Hospital. Informative video frames were extracted using a MATLAB support vector machine (SVM) model and classified as bleeding, polypectomy, tool, residue, thin wrinkle, folded wrinkle, or common. Thin wrinkle, folded wrinkle, and common frames were reanalyzed using SVM for polyp detection. The SVM model was applied hierarchically for effective classification and optimization of the SVM. RESULTS: The mean classification accuracy according to type was over 93%; sensitivity was over 87%. The mean sensitivity for polyp detection was 82.1%, and the positive predicted value (PPV) was 39.3%. Polyps detected using the system were larger (6.3 ± 6.4 vs. 4.9 ± 2.5 mm; P = 0.003) with a more pedunculated morphology (Yamada type III, 10.2 vs. 0%; P < 0.001; Yamada type IV, 2.8 vs. 0%; P < 0.001) than polyps missed by the system. There were no statistically significant differences in polyp distribution or histology between the groups. Informative frames and suspected polyps were presented on a timeline. This summary was evaluated using the system usability scale questionnaire; 89.3% of participants expressed positive opinions. CONCLUSIONS: We developed and verified a system to extract meaningful information from colonoscopy videos. Although further improvement and validation of the system is needed, the proposed system is useful for physicians and patients.

Effects of prolonged exercise versus multiple short exercise sessions on risk for metabolic syndrome and the atherogenic index in middle-aged obese women: a randomised controlled trial.

BACKGROUND: Many people, although they may recognise the positive effects of exercise, do not exercise regularly owing to lack of time. This study aimed to investigate the effects of prolonged single-session exercise and multiple short sessions of exercise on the risk of metabolic syndrome and the atherogenic index in middle-aged obese women. METHODS: Thirty-six participants were divided into the single-session group, multiple-session group, and control group. The single-session group engaged in one session of treadmill exercise for 30 min a day; the multiple-session group had three sessions of 10 min a day. Both groups exercised 3 days/week for 12 weeks. The control group did not perform any exercise. RESULTS: The single-session group showed decreases in weight (0.97 kg [95% C.I. = 0.09-1.83], p < .05), body mass index (0.43 kg/m2 [95% C.I. = 0.03-0.81], p < .05), and fat mass (1.65 kg, [95% C.I. = 0.78-2.51], p < .01). Systolic blood pressure dropped in the single-session group (6.66 mmHg, [95% C.I. = 1.44-11.88], p < .05), and diastolic blood pressure dropped in the multiple-session group (3.38 mmHg, [95% C.I. = 1.44-5.88], p < .01). High-density lipoprotein cholesterol rose in the single-session group (4.08 mg/dL, [95% C.I. = -8.08-(-)0.07], p < .05) and dropped in the control group (10.75 mg/dL [95% C.I. = 1.95-19.54], p < .01). According to post hoc analysis, high-density lipoprotein cholesterol increased more in the single-session group than the control group (95% C.I. = 0.61-21.88, p < .05). Glucose levels decreased in both the single-session group (16 mg/dL [95% C.I. = 5.64-26.35], p < .01) and the multiple-session group (12.16 mg/dL, [95% C.I. = 2.18-22.14], p < .05). Waist circumference decreased in the single-session group (2.65 cm [95% C.I. = 1.46-3.83], p < .001) and multiple-session group (2.04 cm, [95% C.I. = 1.51-2.73], p < .001). Low-density lipoprotein cholesterol levels rose in both the multiple-session group (-15.79 mg/dL [95% C.I. = -34.24-(-)3.78], p < .05) and the control group (-22.94 mg/dL [95% C.I. = -44.63-(-)1.24], p < .05). The atherogenic index increased in the control group (-1.06 [95% C.I. = -1.69-(-)0.41], p < .01). CONCLUSIONS: The findings indicate that prolonged exercise is superior to multiple short sessions for improving the risk of metabolic syndrome and the atherogenic index in middle-aged obese women. However, multiple short sessions can be recommended as an alternative to prolonged exercise when the goal is to decrease blood glucose or waist circumference.

Oxygen saturation and perfusion index from pulse oximetry in adult volunteers with viable incisors.

OBJECTIVE: Evaluation of pulp vitality is an important diagnostic procedure in dentistry. Conventional techniques for measurement of pulp vitality, including thermal stimulation, electrical stimulation, or direct dentin stimulation, are frequently associated with false positive or false negative results. Recently, oxygen saturation from pulse oximetry has been utilized in the evaluation of pulp vitality. Perfusion index (PI) data calculated from photoplethysmography have been widely used to evaluate peripheral perfusion. The combination of oxygen saturation and PI may aid in the accurate measurement of pulp vitality. We aimed to investigate the baseline values of oxygen saturation and PI using pulse oximetry in adult volunteers. MATERIAL AND METHODS: Fifteen adult volunteers with viable incisors were tested. To measure PI, a fabricated oxygen sensor was applied to an incisor without a pulp lesion while oxygen saturation was simultaneously measured in the finger. Oxygen saturation and PI were continuously measured with customized software. The normal reference values of oxygen saturation and PI were obtained by analyzing the recorded data. RESULTS: Pulse oximetry showed relatively stable, objective, and accurate oxygen saturation results. The tooth oxygen saturation ranged from 97% to 100%. The PI ranged from 0.3% to 0.5%, and PI and oxygen saturation showed relatively consistent values across subjects. CONCLUSIONS: Although there are some limitations to our study, these results may prove useful for detecting teeth with impaired vitality and non-invasively differentiating between necrotic and vital pulp.

Effect of Self-Training Using Virtual Reality Head-Mounted Display Simulator on the Acquisition of Holmium Laser Enucleation of the Prostate Surgical Skills.

PURPOSE: We aimed to evaluate the effect of self-training using a virtual reality head-mounted display simulator on the acquisition of surgical skills for holmium laser enucleation surgery. METHODS: Thirteen medical students without surgical skills for holmium laser enucleation of the prostate were trained using multimedia to learn the technique via simulator manipulation. Thereafter, participants performed the technique on a virtual benign prostatic hyperplasia model A (test A). After a 1-week wash-out period, they underwent self-training using a simulator and performed the technique on model B (test B). Subsequently, participants were asked to respond to Training Satisfaction Questions. Video footage of hand movements and endoscope view were recorded during tests A and B for later review by 2 expert surgeons. A 20-step Assessment Checklist, 6-domain Global Rating Scale, and a Pass Rating were used to compare performance on tests A and B. RESULTS: Thirteen participants completed both tests A and B. The 20-step Assessment Checklist and 6-domain Global Rating Scale evaluation results showed significantly improved scores in test B than in test A (P<0.05). No evaluator rated participants as passed after test A, but 11 participants (84.6%) passed after test B. Ten participants (76.9%) indicated that the simulator was helpful in acquiring surgical skills for holmium laser enucleation of the prostate. CONCLUSION: The virtual reality head-mounted display holmium laser enucleation of the prostate simulator was effective for surgical skill training. This simulator may help to shorten the learning curve of this technique in real clinical practice in the future.

Immersive virtual reality simulation training for cesarean section: a randomized controlled trial.

BACKGROUND: Caesarean section (CS) is a complex surgical procedure that involves many steps and requires careful precision. Virtual reality (VR) simulation has emerged as a promising tool for medical education and training, providing a realistic and immersive environment for learners to practice clinical skills and decision-making. This study aimed to evaluate the educational effectiveness of a VR simulation program in training the management of patients with premature rupture of membranes (PROM) and CS. MATERIALS AND METHODS: A two-arm parallel randomized controlled trial was conducted with 105 eligible participants randomly assigned to the VR group ( n =53) or the control group ( n =52) in a 1:1 ratio. The VR group received VR simulation training focused on PROM management and CS practice, while the control group watched a video presentation with narrative of clinical scenario and recording of CS. Both groups completed questionnaires assessing their prior experiences with VR, experience in managing patients with PROM and performing CS, as well as their confidence levels. These questionnaires were administered before and after the intervention, along with a mini-test quiz. RESULTS: Baseline characteristics and previous experiences were comparable between the two groups. After the intervention, the VR group had higher confidence scores in all four aspects, including managing patients with PROM, performing CS as an operator, and understanding the indications and complications of CS, compared to the control group. The VR group also achieved significantly higher scores on the mini-test quiz [median (interquartile range), 42 (37-48) in the VR group; 36 (32-40) in the control group, P <0.001]. CONCLUSION: VR simulation program can be an effective educational tool for improving participants' knowledge and confidence in managing patients with PROM and performing CS.

Navigating the Future of 3D Laparoscopic Liver Surgeries: Visualization of Internal Anatomy on Laparoscopic Images With Augmented Reality.

INTRODUCTION: Liver tumor resection requires precise localization of tumors and blood vessels. Despite advancements in 3-dimensional (3D) visualization for laparoscopic surgeries, challenges persist. We developed and evaluated an augmented reality (AR) system that overlays preoperative 3D models onto laparoscopic images, offering crucial support for 3D visualization during laparoscopic liver surgeries. METHODS: Anatomic liver structures from preoperative computed tomography scans were segmented using open-source software including 3D Slicer and Maya 2022 for 3D model editing. A registration system was created with 3D visualization software utilizing a stereo registration input system to overlay the virtual liver onto laparoscopic images during surgical procedures. A controller was customized using a modified keyboard to facilitate manual alignment of the virtual liver with the laparoscopic image. The AR system was evaluated by 3 experienced surgeons who performed manual registration for a total of 27 images from 7 clinical cases. The evaluation criteria included registration time; measured in minutes, and accuracy; measured using the Dice similarity coefficient. RESULTS: The overall mean registration time was 2.4±1.7 minutes (range: 0.3 to 9.5 min), and the overall mean registration accuracy was 93.8%±4.9% (range: 80.9% to 99.7%). CONCLUSION: Our validated AR system has the potential to effectively enable the prediction of internal hepatic anatomic structures during 3D laparoscopic liver resection, and may enhance 3D visualization for select laparoscopic liver surgeries.

Development of Prediction Model for Intensive Care Unit Admission Based on Heart Rate Variability: A Case-Control Matched Analysis.

This study aimed to develop a predictive model for intensive care unit (ICU) admission by using heart rate variability (HRV) data. This retrospective case-control study used two datasets (emergency department [ED] patients admitted to the ICU, and patients in the operating room without ICU admission) from a single academic tertiary hospital. HRV metrics were measured every 5 min using R-peak-to-R-peak (R-R) intervals. We developed a generalized linear mixed model to predict ICU admission and assessed the area under the receiver operating characteristic curve (AUC). Odds ratios (ORs) with 95% confidence intervals (CIs) were calculated from the coefficients. We analyzed 610 (ICU: 122; non-ICU: 488) patients, and the factors influencing the odds of ICU admission included a history of diabetes mellitus (OR [95% CI]: 3.33 [1.71-6.48]); a higher heart rate (OR [95% CI]: 3.40 [2.97-3.90] per 10-unit increase); a higher root mean square of successive R-R interval differences (RMSSD; OR [95% CI]: 1.36 [1.22-1.51] per 10-unit increase); and a lower standard deviation of R-R intervals (SDRR; OR [95% CI], 0.68 [0.60-0.78] per 10-unit increase). The final model achieved an AUC of 0.947 (95% CI: 0.906-0.987). The developed model effectively predicted ICU admission among a mixed population from the ED and operating room.

Density clustering-based automatic anatomical section recognition in colonoscopy video using deep learning.

Recognizing anatomical sections during colonoscopy is crucial for diagnosing colonic diseases and generating accurate reports. While recent studies have endeavored to identify anatomical regions of the colon using deep learning, the deformable anatomical characteristics of the colon pose challenges for establishing a reliable localization system. This study presents a system utilizing 100 colonoscopy videos, combining density clustering and deep learning. Cascaded CNN models are employed to estimate the appendix orifice (AO), flexures, and "outside of the body," sequentially. Subsequently, DBSCAN algorithm is applied to identify anatomical sections. Clustering-based analysis integrates clinical knowledge and context based on the anatomical section within the model. We address challenges posed by colonoscopy images through non-informative removal preprocessing. The image data is labeled by clinicians, and the system deduces section correspondence stochastically. The model categorizes the colon into three sections: right (cecum and ascending colon), middle (transverse colon), and left (descending colon, sigmoid colon, rectum). We estimated the appearance time of anatomical boundaries with an average error of 6.31 s for AO, 9.79 s for HF, 27.69 s for SF, and 3.26 s for outside of the body. The proposed method can facilitate future advancements towards AI-based automatic reporting, offering time-saving efficacy and standardization.

Development and evaluation of an automatic acne lesion detection program using digital image processing.

BACKGROUND/PURPOSE: Existing acne grading methods, which depend on overall impression, require a long training period and there is a high degree of variability among raters, including trained dermatologists. The use of lesion count provides fair reproducibility but the method is time consuming. New technologies in photographic equipment and software allow solutions to the problem of acne evaluation. This study was conducted to develop the automatic acne lesion program and evaluation of its usefulness. METHODS: We made the conditions to optimize characterization of acne lesions and developed the counting program. Twenty-five volunteers with acne lesions were enrolled. Automated lesion counting for five subtypes of acne (papule, nodule, pustule, whitehead comedone, and blackhead comedone) was performed with image processing. The usefulness of the automatic lesion count program was assessed by a comparison with manual counting performed by an expert dermatologist. RESULTS: In a comparison with manual counting performed by an expert dermatologist, the sensitivity and positive predictive value of the lesion-counting program was greater than 70% for papules, nodules, pustules, and whitehead comedo. In a comparison with manual counting, findings with the use of the lesion-counting program were well correlated for papules, nodules, pustules, and whitehead comedo (r > 0.9). CONCLUSION: Automatic lesion-counting program can be a useful tool for acne severity evaluation.

Digital Therapeutic Exercises Using Augmented Reality Glasses for Frailty Prevention among Older Adults.

OBJECTIVES: The objective of this study was to investigate the effects of a digital therapeutic exercise platform for pre-frail or frail elderly individuals using augmented reality (AR) technology accessed through glasses. A tablet-based exercise program was utilized for the control group, and a non-inferiority assessment was employed. METHODS: The participants included older adult women aged 65 years and older residing in Incheon, South Korea. A digital therapeutic exercise program involving AR glasses or tablet-based exercise was administered twice a week for 12 weeks, with gradually increasing exercise duration. Statistical analysis was conducted using the t-test and Wilcoxon rank sum test for non-inferiority assessment. RESULTS: In the primary efficacy assessment, regarding the change in lower limb strength, a non-inferior result was observed for the intervention group (mean change, 5.46) relative to the control group (mean change, 4.83), with a mean difference of 0.63 between groups (95% confidence interval, -2.33 to 3.58). Changes in body composition and physical fitness-related variables differed non-significantly between the groups. However, the intervention group demonstrated a significantly greater increase in cardiorespiratory endurance (p < 0.005) and a significantly larger decrease in the frailty index (p < 0.001). CONCLUSIONS: An AR-based digital therapeutic program significantly and positively contributed to the improvement of cardiovascular endurance and the reduction of indicators of aging among older adults. These findings underscore the value of digital therapeutics in mitigating the effects of aging.

Exploring the Category and Use Cases on Digital Therapeutic Methodologies.

OBJECTIVES: As the Fourth Industrial Revolution advances, there is a growing interest in digital technology. In particular, the use of digital therapeutics (DTx) in healthcare is anticipated to reduce medical expenses. However, analytical research on DTx is still insufficient to fuel momentum for future DTx development. The purpose of this article is to analyze representative cases of different types of DTx from around the world and to propose a classification system. METHODS: In this exploratory study examining DTx interaction types and representative cases, we conducted a literature review and selected seven interaction types that were utilized in a large number of cases. Then, we evaluated the specific characteristics of each DTx mechanism by reviewing the relevant literature, analyzing their indications and treatment components. A representative case for each mechanism was provided. RESULTS: Cognitive behavioral therapy, distraction therapy, graded exposure therapy, reminiscence therapy, art therapy, therapeutic exercise, and gamification are the seven categories of DTx interaction types. Illustrative examples of each variety are provided. CONCLUSIONS: Efforts from both the government and private sector are crucial for success, as standardization can decrease both the expense and the time required for government-led DTx development. The private sector should partner with medical facilities to stimulate potential demand, carry out clinical research, and produce scholarly evidence.

Validation of data mining models by comparing with conventional methods for dental age estimation in Korean juveniles and young adults.

Teeth are known to be the most accurate age indicators of human body and are frequently applied in forensic age estimation. We aimed to validate data mining-based dental age estimation, by comparing the accuracy of the estimation and classification performance of 18-year thresholds with conventional methods and with data mining-based age estimation. A total of 2657 panoramic radiographs were collected from Koreans and Japanese populations aged 15 to 23 years. They were subdivided into a training and internal test set of 900 radiographs each from Koreans, and an external test set of 857 radiographs from Japanese. We compared the accuracy and classification performance of the test sets from conventional methods with those from the data mining models. The accuracy of the conventional method with the internal test set was slightly higher than that of the data mining models, with a slight difference (mean absolute error < 0.21 years, root mean square error < 0.24 years). The classification performance of the 18-year threshold was also similar between the conventional method and the data mining models. Thus, conventional methods can be replaced by data mining models in forensic age estimation using second and third molar maturity of Korean juveniles and young adults.

User Experience of Augmented Reality Glasses-based Tele-Exercise in Elderly Women.

OBJECTIVES: The purpose of this study was to identify any difference in user experience between tablet- and augmented reality (AR) glasses-based tele-exercise programs in elderly women. METHODS: Participants in the AR group (n = 14) connected Nreal glasses with smartphones to display a pre-recorded exercise program, while each member of the tablet group (n = 13) participated in the same exercise program using an all-in-one personal computer. The program included sitting or standing on a chair, bare-handed calisthenics, and muscle strengthening using an elastic band. The exercise movements were presented first for the upper and then the lower extremities, and the total exercise time was 40 minutes (5 minutes of warm-up exercises, 30 minutes of main exercises, and 5 minutes of cool-down exercises). To evaluate the user experience, a questionnaire consisting of a 7-point Likert scale was used as a measurement tool. In addition, the Wilcoxon rank-sum test was used to assess differences between the two groups. RESULTS: Of the six user experience scales, attractiveness (p = 0.114), stimulation (p = 0.534), and novelty (p = 0.916) did not differ significantly between the groups. However, efficiency (p = 0.006), perspicuity (p = 0.008), and dependability (p = 0.049) did vary significantly between groups. CONCLUSIONS: When developing an AR glasses-based exercise program for the elderly, the efficiency, clarity, and stability of the program must be considered to meet the participants' needs.