Background: Recently, deep learning techniques have been used in medical imaging studies. We present an algorithm that measures radiologic parameters of distal radius fractures using a deep learning technique and compares the predicted parameters with those measured by an orthopedic hand surgeon. Methods: We collected anteroposterior (AP) and lateral X-ray images of 634 wrists in 624 patients with distal radius fractures treated conservatively with a follow-up of at least 2 months. We allocated 507 AP and 507 lateral images to the training set (80% of the images were used to train the model, and 20% were utilized for validation) and 127 AP and 127 lateral images to the test set. The margins of the radius and ulna were annotated for ground truth, and the scaphoid in the lateral views was annotated in the box configuration to determine the volar side of the images. Radius segmentation was performed using attention U-Net, and the volar/dorsal side was identified using a detection and classification model based on RetinaNet. The proposed algorithm measures the radial inclination, dorsal or volar tilt, and radial height by index axes and points from the segmented radius and ulna. Results: The segmentation model for the radius exhibited an accuracy of 99.98% and a Dice similarity coefficient (DSC) of 98.07% for AP images, and an accuracy of 99.75% and a DSC of 94.84% for lateral images. The segmentation model for the ulna showed an accuracy of 99.84% and a DSC of 96.48%. Based on the comparison of the radial inclinations measured by the algorithm and the manual method, the Pearson correlation coefficient was 0.952, and the intraclass correlation coefficient was 0.975. For dorsal/ volar tilt, the correlation coefficient was 0.940, and the intraclass correlation coefficient was 0.968. For radial height, it was 0.768 and 0.868, respectively. Conclusions The deep learning-based algorithm demonstrated excellent segmentation of the distal radius and ulna in AP and lateral radiographs of the wrist with distal radius fractures and afforded automatic measurements of radiologic parameters.

Background: We aimed to evaluate whether composite blood biomarkers including aldo-keto reductase family 1 member B10 (AKR1B10) and cytokeratin 18 (CK-18; a nonalcoholic steatohepatitis [NASH] marker) have clinically applicable performance for the diagnosis of NASH, advanced liver fibrosis, and high-risk NASH (NASH+significant fibrosis). Methods: A total of 116 subjects including healthy control subjects and patients with biopsy-proven nonalcoholic fatty liver disease (NAFLD) were analyzed to assess composite blood-based and imaging-based biomarkers either singly or in combination. Results: A composite blood biomarker comprised of AKR1B10, CK-18, aspartate aminotransferase (AST), and alanine aminotransferase (ALT) showed excellent performance for the diagnosis of, NASH, advanced fibrosis, and high-risk NASH, with area under the receiver operating characteristic curve values of 0.934 (95% confidence interval [CI], 0.888 to 0.981), 0.902 (95% CI, 0.832 to 0.971), and 0.918 (95% CI, 0.862 to 0.974), respectively. However, the performance of this blood composite biomarker was inferior to that various magnetic resonance (MR)-based composite biomarkers, such as proton density fat fraction/MR elastography- liver stiffness measurement (MRE-LSM)/ALT/AST for NASH, MRE-LSM+fibrosis-4 index for advanced fibrosis, and the known MR imaging-AST (MAST) score for high-risk NASH. Conclusion Our blood composite biomarker can be useful to distinguish progressive forms of NAFLD as an initial noninvasive test when MR-based tools are not available.

Objectives: This study was conducted to develop a mobile application for digital health coaching to support self-management in older adults with multiple chronic conditions. Additionally, the usability of this application was evaluated. Methods: The HAHA2022 mobile application was developed through a multidisciplinary team approach, incorporating digital health coaching strategies targeting community-dwelling older adults with multiple chronic conditions. Usability was assessed with the Korean version of the Mobile Application Rating Scale. The usability tests involved eight expert panel members and 10 older adults (mean age, 74 ± 3 years; 90% women) from one senior welfare center. Results: HAHA2022 is an Android-based mobile application that is also integrated into wearable devices to track physical activity. It features an age-friendly design and includes five main menus: Home, Action Plan, Education, Health Log, and Community. The average overall usability test scores—covering engagement, functionality, aesthetics, and information—were 4.27 of 5 for the expert panel and 4.53 of 5 for the older adults. Conclusions The HAHA2022 application was developed to improve self-management among communitydwelling older adults with multiple chronic conditions. Usability tests indicate that the application is highly acceptable and feasible for use by this population. Consequently, HAHA2022 is anticipated to be widely implemented. Nonetheless, further research is required to confirm its effectiveness through digital health intervention.

Background: Atrial fibrillation (AF) is a prevalent cardiac arrhythmia associated with significant morbidity and mortality, posing a considerable burden on healthcare systems. In Republic of Korea, the prevalence and incidence of AF have increased in recent years. There have also been significant changes in the trends of antiarrhythmic drug (AAD) use and procedural treatments for AF. Objectives: This study aims to review the trends in AF treatment strategies in Republic of Korea, particularly focusing on the utilization of antiarrhythmic drugs and catheter ablation. Methods: The Korean National Health Insurance Service (K-NHIS) data were used to identify AF patients from 2013 to 2022. AAD usage and catheter ablation procedures were analyzed annually. AADs were classified into Class IC and III drugs. Trends in beta-blockers, calcium channel blockers, and digoxin prescriptions were also examined. The primary endpoint was the trend of AAD use and AF catheter ablation (AFCA) over 10 years. Results: In 2022, 940,063 patients had a prior diagnosis of AF. From 2013 to 2022, the use of AADs increased from 12.1 to 16.4% among prevalent AF patients. Beta-blockers were the most commonly prescribed rate control medication, while the use of calcium channel blockers and digoxin declined. The frequency of AFCA procedures also increased, from 0.5% of prevalent AF patients in 2013 to 0.7% in 2022. Younger patients, males, and those with lower CHA2DS2-VASc scores were more likely to receive AFCA. Regional variations in treatment patterns were observed, with Seoul exhibiting higher rates of procedural treatments and AAD prescriptions. Conclusions Over the past decade, there has been a significant increase in the use of AADs and AFCA procedures in Republic of Korea. These trends reflect recent advancements in AF management advocating a refined rhythm control strategy.

Objectives: Since the easiest way to identify pills and obtain information about them is to distinguish them visually, many studies on image processing technology exist. However, no automatic system for generating pill image data has yet been developed. Therefore, we propose a system for automatically generating image data by taking pictures of pills from various angles. This system is referred to as the pill filming system in this paper. Methods: We designed the pill filming system to have three components: structure, controller, and a graphical user interface (GUI). This system was manufactured with black polylactic acid using a 3D printer for lightweight and easy manufacturing. The mainboard controls data storage, and the entire process is managed through the GUI. After one reciprocating movement of the seesaw, the web camera at the top shoots the target pill on the stage. This image is then saved in a specific directory on the mainboard. Results: The pill filming system completes its workflow after generating 300 pill images. The total time to collect data per pill takes 21 minutes and 25 seconds. The generated image size is 1280 × 960 pixels, the horizontal and vertical resolutions are both 96 DPI (dot per inch), and the file extension is .jpg. Conclusions This paper proposes a system that can automatically generate pill image data from various angles. The pill observation data from various angles include many cases. In addition, the data collected in the same controlled environment have a uniform background, making it easy to process the images. Large quantities of high-quality data from the pill filming system can contribute to various studies using pill images.

Objective: : Deep learning is a machine learning approach based on artificial neural network training, and object detection algorithm using deep learning is used as the most powerful tool in image analysis. We analyzed and evaluated the diagnostic performance of a deep learning algorithm to identify skull fractures in plain radiographic images and investigated its clinical applicability. Methods: : A total of 2026 plain radiographic images of the skull (fracture, 991; normal, 1035) were obtained from 741 patients. The RetinaNet architecture was used as a deep learning model. Precision, recall, and average precision were measured to evaluate the deep learning algorithm’s diagnostic performance. Results: : In ResNet-152, the average precision for intersection over union (IOU) 0.1, 0.3, and 0.5, were 0.7240, 0.6698, and 0.3687, respectively. When the intersection over union (IOU) and confidence threshold were 0.1, the precision was 0.7292, and the recall was 0.7650. When the IOU threshold was 0.1, and the confidence threshold was 0.6, the true and false rates were 82.9% and 17.1%, respectively. There were significant differences in the true/false and false-positive/false-negative ratios between the anteriorposterior, towne, and both lateral views (p=0.032 and p=0.003). Objects detected in false positives had vascular grooves and suture lines. In false negatives, the detection performance of the diastatic fractures, fractures crossing the suture line, and fractures around the vascular grooves and orbit was poor. Conclusion : The object detection algorithm applied with deep learning is expected to be a valuable tool in diagnosing skull fractures.

Objectives: The objective of this study was to investigate the factors associated with website operation among medical facilities. Methods: A cross-sectional study design was employed to investigate 1,519 hospitals, 33,043 medical clinics (MCs), and 18,240 dental clinics (DCs) as of 2020. The main outcome variable was analyzed according to technological, organizational, and environmental factors. Results: The percentages of small hospitals, MCs, and DCs with websites were 26.4%, 9.0%, and 6.6%, respectively. For small hospitals, the nearby presence of a subway station (odds ratio [OR] = 2.772; 95% confidence interval [CI], 1.973–3.892; p < 0.0001) was the only factor significantly associated with website operation status. Among medical and dental clinics, the percentage of specialists—MCs (OR = 1.002; 95% CI, 1.000–1.004; p = 0.0175) and DCs (OR = 1.002; 95% CI, 1.001–1.004; p = 0.0061), the nearby presence of a subway station—MCs (OR = 2.954; 95% CI, 2.613–3.339; p < 0.0001) and DCs (OR = 3.444; 95% CI, 2.945–4.028; p < 0.0001), and the number of clinics in the local area—MCs (OR = 1.029; 95% CI, 1.026–1.031; p < 0.0001) and DCs (OR = 1.080; 95% CI, 1.066–1.093; p < 0.0001)—were significantly associated with website operation. Conclusions Clinics are critically affected by internal and external factors regarding website operation relative to small hospitals. Healthcare policymakers involved with information technologies may need to pay attention to those factors associated with website dispersion among small clinics.

Objectives: Various sensory symptoms have been recognized after COVID-19 vaccination. Here, we aimed to explore the association between the suggestive symptom of restless legs syndrome (RLSss) and COVID-19 vaccination using an online survey. Methods: We prospectively studied participants who were working in our hospital after at least the first dose of the ChAdOx1 or BNT162b2 mRNA vaccine. The participants were invited via smartphone messages and voluntarily filled out an online questionnaire that included adverse events after vaccination. We considered the participants as having RLSss if they reported that they had three or more symptoms in the restless legs syndrome (RLS) diagnostic criteria. Results: A total of 628 participants (506 female; mean age, 37.7±12.4 years) responded fully to our online survey. 588 participants (93.6%) received the first dose of the ChAdOx1 vaccine (BNT162b2 mRNA vaccine for 40 participants). A total of 44 out of the 628 participants (7.0%) reported that they had RLSss. Myalgia was more common in participants with RLSss than in those without RLSss (97.7% vs. 67.3%, p<0.001). Multivariate testing showed that age (odds ratio, 1.037 per 1 year increase; 95% CI, 1.004–1.071) and the presence of myalgia (odds ratio, 20.479; 95% CI, 4.266–368.206) were associated with the presence of RLSss. Conclusions This pilot study explored RLSss after COVID-19 vaccination and the results suggested that RLS might be one of the causes of adverse symptoms after COVID-19 vaccination. Further studies are required to confirm the relationship between RLS and COVID-19 vaccination.

Purpose: In this paper, we propose deep-learning methodology with which to enhance the mass differentiation performance of convolutional neural network (CNN)-based architecture. Materials and Methods: We differentiated breast mass lesions from gray-scale X-ray mammography images based on regions of interest (ROIs). Our dataset comprised breast mammogram images for 150 cases of malignant masses from which we extracted the mass ROI, and we composed a CNN-based deep learning model trained on this dataset to identify ROI mass lesions. The test dataset was created by shifting some of the training data images. Thus, although both datasets were different, they retained a deep structural similarity. We then applied our trained deep-learning model to detect masses on 8-bit mammogram images containing malignant masses. The input images were preprocessed by applying a scaling parameter of intensity before being used to train the CNN model for mass differentiation. Results: The highest area under the receiver operating characteristic curve was 0.897 (Î 20). Conclusion Our results indicated that the proposed patch-wise detection method can be utilized as a mass detection and segmentation tool.

Objectives: As endoscopic, laparoscopic, and robotic surgical procedures become more common, surgical videos are increasingly being treated as records and serving as important data sources for education, research, and developing new solutions with recent advances in artificial intelligence (AI). However, most hospitals do not have a system that can store and manage such videos systematically. This study aimed to develop a system to help doctors manage surgical videos and turn them into content and data. Methods: We developed a video archiving and communication system (VACS) to systematically process surgical videos. The VACS consists of a video capture device called SurgBox and a video archiving system called SurgStory. SurgBox automatically transfers surgical videos recorded in the operating room to SurgStory. SurgStory then analyzes the surgical videos and indexes important sections or video frames to provide AI reports. It allows doctors to annotate classified indexing frames, “data-ize” surgical information, create educational content, and communicate with team members. Results: The VACS collects surgical and procedural videos, and helps users manage archived videos. The accuracy of a convolutional neural network learning model trained to detect the top five surgical instruments reached 96%. Conclusions With the advent of the VACS, the informational value of medical videos has increased. It is possible to improve the efficiency of doctors’ continuing education by making video-based online learning more active and supporting research using data from medical videos. The VACS is expected to promote the development of new AI-based products and services in surgical and procedural fields.