No abstract available.
Delivery of Health Care*

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 theprimary 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.

Purpose: Bladder capacity is an important parameter in the diagnosis of lower urinary tract dysfunction. We aimed to determine whether the maximum bladder capacity (MCC) measured during a urodynamic study was affected by involuntary detrusor contraction (IDC) in patients with Lower Urinary Tract Symptoms (LUTS)/Benign Prostatic Hyperplasia (BPH). Methods: Between March 2020 and April 2021, we obtained maximum voided volume (MVV) from a 3-day frequency-volume chart, MCC during filling cystometry, and maximum anesthetic bladder capacity (MABC) during holmium laser enucleation of the prostate under spinal or general anesthesia in 139 men with LUTS/BPH aged >50 years. Patients were divided according to the presence of IDC during filling cystometry. We assumed that the MABC is close to the true value of the MCC, as it is measured under the condition of minimizing neural influence over the bladder. Results: There was no difference in demographic and clinical characteristics between the non-IDC (n=20) and IDC groups (n=119) (mean age, 71.5±7.4) (P>0.05). The non-IDC group had greater bladder volume to feel the first sensation, first desire, and strong desire than the IDC group (P<0.001). In all patients, MABC and MVV were correlated (r=0.41, P<0.001); however, there was no correlation between MCC and MABC (r=0.19, P=0.02). There was no significant difference in MABC between the non-IDC and IDC groups (P=0.19), but MVV and MCC were significantly greater in the non-IDC group (P<0.001). There was no significant difference between MABC and MVV (MABC-MVV, P=0.54; MVV/MABC, P=0.07), but there was a significant difference between MABC and MCC between the non-IDC and IDC groups (MABC-MCC, P<0.001; MCC/MABC, P<0.001). Conclusions Maximum bladder capacity from a urodynamic study does not represent true bladder capacity because of involuntary contractions.

OBJECTIVES: Augmented reality (AR) technology has become rapidly available and is suitable for various medical applications since it can provide effective visualization of intricate anatomical structures inside the human body. This paper describes the procedure to develop an AR app with Unity3D and Vuforia software development kit and publish it to a smartphone for the localization of critical tissues or organs that cannot be seen easily by the naked eye during surgery. METHODS: In this study, Vuforia version 6.5 integrated with the Unity Editor was installed on a desktop computer and configured to develop the Android AR app for the visualization of internal organs. Three-dimensional segmented human organs were extracted from a computerized tomography file using Seg3D software, and overlaid on a target body surface through the developed app with an artificial marker. RESULTS: To aid beginners in using the AR technology for medical applications, a 3D model of the thyroid and surrounding structures was created from a thyroid cancer patient's DICOM file, and was visualized on the neck of a medical training mannequin through the developed AR app. The individual organs, including the thyroid, trachea, carotid artery, jugular vein, and esophagus were localized by the surgeon's Android smartphone. CONCLUSIONS: Vuforia software can help even researchers, students, or surgeons who do not possess computer vision expertise to easily develop an AR app in a user-friendly manner and use it to visualize and localize critical internal organs without incision. It could allow AR technology to be extensively utilized for various medical applications.
Carotid Arteries ; Education, Medical ; Esophagus ; Human Body ; Humans ; Imaging, Three-Dimensional ; Jugular Veins ; Manikins ; Methyltestosterone ; Neck ; Smartphone ; Surgeons ; Thyroid Gland ; Thyroid Neoplasms ; Thyroidectomy ; Trachea

OBJECTIVES: Customer discovery (CD) is a method to determine if there are actual customers for a product/service and what they would want before actually developing the product/service. This concept, however, is rather new to health information technology (IT) systems. Therefore, the aim of this paper was to demonstrate how to use the CD method in developing a comprehensive health IT service for patients with knee/leg pain. METHODS: We participated in a 6-week I-Corps program to perform CD, in which we interviewed 55 people in person, by phone, or by video conference within 6 weeks: 4 weeks in the United States and 2 weeks in Korea. The interviewees included orthopedic doctors, physical therapists, physical trainers, physicians, researchers, pharmacists, vendors, and patients. By analyzing the interview data, the aim was to revise our business model accordingly. RESULTS: Using the CD approach enabled us to understand the customer segments and identify value propositions. We concluded that a facilitating tele-rehabilitation system is needed the most and that the most suitable customer segment is early stage arthritis patients. We identified a new design concept for the customer segment. Furthermore, CD is required to identify value propositions in detail. CONCLUSIONS: CD is crucial to determine a more desirable direction in developing health IT systems, and it can be a powerful tool to increase the potential for successful commercialization in the health IT field.
Arthritis ; Commerce ; Entrepreneurship ; Health Services Needs and Demand ; Humans ; Korea ; Medical Informatics ; Methods ; Orthopedics ; Pharmacists ; Physical Therapists ; Qualitative Research ; Telerehabilitation ; United States

OBJECTIVES: This study proposes a method for classifying three types of resting membrane potential signals obtained as images through diagnostic needle electromyography (EMG) using TensorFlow-Slim and Python to implement an artificial-intelligence-based image recognition scheme. METHODS: Waveform images of an abnormal resting membrane potential generated by diagnostic needle EMG were classified into three types—positive sharp waves (PSW), fibrillations (Fibs), and Others—using the TensorFlow-Slim image classification model library. A total of 4,015 raw waveform data instances were reviewed, with 8,576 waveform images subsequently collected for training. Images were learned repeatedly through a convolutional neural network. Each selected waveform image was classified into one of the aforementioned categories according to the learned results. RESULTS: The classification model, Inception v4, was used to divide waveform images into three categories (accuracy = 93.8%, precision = 99.5%, recall = 90.8%). This was done by applying the pretrained Inception v4 model to a fine-tuning method. The image recognition model was created for training using various types of image-based medical data. CONCLUSIONS: The TensorFlow-Slim library can be used to train and recognize image data, such as EMG waveforms, through simple coding rather than by applying TensorFlow. It is expected that a convolutional neural network can be applied to image data such as the waveforms of electrophysiological signals in a body based on this study.
Artificial Intelligence ; Boidae ; Classification ; Clinical Coding ; Electromyography ; Membrane Potentials ; Methods ; Needles

Objectives: Smart hospitals involve the application of recent information and communications technology (ICT) innovations to medical services; however, the concept of a smart hospital has not been rigorously defined. In this study, we aimed to derive the definition and service types of smart hospitals and investigate cases of each type. Methods: A literature review was conducted regarding the background and technical characteristics of smart hospitals. On this basis, we conducted a focus group interview with experts in hospital information systems, and ultimately derived eight smart hospital service types. Results: Smart hospital services can be classified into the following types: services based on location recognition and tracking technology that measures and monitors the location information of an object based on short-range communication technology; high-speed communication network-based services based on new wireless communication technology; Internet of Things-based services that connect objects embedded with sensors and communication functions to the internet; mobile health services such as mobile phones, tablets, and wearables; artificial intelligence-based services for the diagnosis and prediction of diseases; robot services provided on behalf of humans in various medical fields; extended reality services that apply hyper-realistic immersive technology to medical practice; and telehealth using ICT. Conclusions Smart hospitals can influence health and medical policies and create new medical value by defining and quantitatively measuring detailed indicators based on data collected from existing hospitals. Simultaneously, appropriate government incentives, consolidated interdisciplinary research, and active participation by industry are required to foster and facilitate smart hospitals.

Objectives: The purpose of this study was to explore new ways of creating value in the medical field and to derive recommendations for the role of medical institutions and the government. Methods: In this paper, based on expert discussion, we classified Internet of Things (IoT) technologies into four categories according to the type of information they collect (location, environmental parameters, energy consumption, and biometrics), and investigated examples of application. Results: Biometric IoT diagnoses diseases accurately and offers appropriate and effective treatment. Environmental parameter measurement plays an important role in accurately identifying and controlling environmental factors that could be harmful to patients. The use of energy measurement and location tracking technology enabled optimal allocation of limited hospital resources and increased the efficiency of energy consumption. The resulting economic value has returned to patients, improving hospitals’ cost-effectiveness. Conclusions Introducing IoT-based technology to clinical sites, including medical institutions, will enhance the quality of medical services, increase patient safety, improve management efficiency, and promote patient-centered medical services. Moreover, the IoT is expected to play an active role in the five major tasks of facility hygiene in medical fields, which are all required to deal with the COVID-19 pandemic: social distancing, contact tracking, bed occupancy control, and air quality management. Ultimately, the IoT is expected to serve as a key element for hospitals to perform their original functions more effectively. Continuing investments, deregulation policies, information protection, and IT standardization activities should be carried out more actively for the IoT to fulfill its expectations.

Objectives: This paper proposes a method for computer-assisted diagnosis of coronavirus disease 2019 (COVID-19) through chest X-ray imaging using a deep learning model without writing a single line of code using the Konstanz Information Miner (KNIME) analytics platform. Methods: We obtained 155 samples of posteroanterior chest X-ray images from COVID-19 open dataset repositories to develop a classification model using a simple convolutional neural network (CNN). All of the images contained diagnostic information for COVID-19 and other diseases. The model would classify whether a patient was infected with COVID-19 or not. Eighty percent of the images were used for model training, and the rest were used for testing. The graphic user interface-based programming in the KNIME enabled class label annotation, data preprocessing, CNN model training and testing, performance evaluation, and so on. Results: 1,000 epochs training were performed to test the simple CNN model. The lower and upper bounds of positive predictive value (precision), sensitivity (recall), specificity, and f-measure are 92.3% and 94.4%. Both bounds of the model’s accuracies were equal to 93.5% and 96.6% of the area under the receiver operating characteristic curve for the test set. Conclusions In this study, a researcher who does not have basic knowledge of python programming successfully performed deep learning analysis of chest x-ray image dataset using the KNIME independently. The KNIME will reduce the time spent and lower the threshold for deep learning research applied to healthcare.