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.

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.

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.

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.

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.

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.

Deep Learning of Speech Data for Early Detection of Alzheimer's Disease in the Elderly

BACKGROUND

Alzheimer's disease (AD) is the most common form of dementia, which makes the lives of patients and their families difficult for various reasons. Therefore, early detection of AD is crucial to alleviating the symptoms through medication and treatment.

OBJECTIVE

Given that AD strongly induces language disorders, this study aims to detect AD rapidly by analyzing the language characteristics.

MATERIALS AND METHODS

The mini-mental state examination for dementia screening (MMSE-DS), which is most commonly used in South Korean public health centers, is used to obtain negative answers based on the questionnaire. Among the acquired voices, significant questionnaires and answers are selected and converted into mel-frequency cepstral coefficient (MFCC)-based spectrogram images. After accumulating the significant answers, validated data augmentation was achieved using the Densenet121 model. Five deep learning models, Inception v3, VGG19, Xception, Resnet50, and Densenet121, were used to train and confirm the results.

RESULTS

Considering the amount of data, the results of the five-fold cross-validation are more significant than those of the hold-out method. Densenet121 exhibits a sensitivity of 0.9550, a specificity of 0.8333, and an accuracy of 0.9000 in a five-fold cross-validation to separate AD patients from the control group.

CONCLUSIONS

The potential for remote health care can be increased by simplifying the AD screening process. Furthermore, by facilitating remote health care, the proposed method can enhance the accessibility of AD screening and increase the rate of early AD detection.

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.

Automated Facial Acne Lesion Detecting and Counting Algorithm for Acne Severity Evaluation and Its Utility in Assisting Dermatologists

BACKGROUND

Although lesion counting is an evaluation method that effectively analyzes facial acne severity, its usage is limited because of difficult implementation.

OBJECTIVES

We aimed to develop and validate an automated algorithm that detects and counts acne lesions by type, and to evaluate its clinical applicability as an assistance tool through a reader test.

METHODS

A total of 20,699 lesions (closed and open comedones, papules, nodules/cysts, and pustules) were manually labeled on 1213 facial images of 398 facial acne photography sets (frontal and both lateral views) acquired from 258 patients and used for training and validating algorithms based on a convolutional neural network for classifying five classes of acne lesions or for binary classification into noninflammatory and inflammatory lesions.

RESULTS

In the validation dataset, the highest mean average precision was 28.48 for the binary classification algorithm. Pearson's correlation of lesion counts between algorithm and ground-truth was 0.72 (noninflammatory) and 0.90 (inflammatory), respectively. In the reader test, eight readers (100.0%) detected and counted lesions more accurately using the algorithm compared with the reader-alone evaluation.

CONCLUSIONS

Overall, our algorithm demonstrated clinically applicable performance in detecting and counting facial acne lesions by type and its utility as an assistance tool for evaluating acne severity.

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.

Investigation of optimal convolutional neural network conditions for thyroid ultrasound image analysis

Neural network models have been used to analyze thyroid ultrasound (US) images and stratify malignancy risk of the thyroid nodules. We investigated the optimal neural network condition for thyroid US image analysis. We compared scratch and transfer learning models, performed stress tests in 10% increments, and compared the performance of three threshold values. All validation results indicated superiority of the transfer learning model over the scratch model. Stress test indicated that training the algorithm using 3902 images (70%) resulted in a performance which was similar to the full dataset (5575). Threshold 0.3 yielded high sensitivity (1% false negative) and low specificity (72% false positive), while 0.7 gave low sensitivity (22% false negative) and high specificity (23% false positive). Here we showed that transfer learning was more effective than scratch learning in terms of area under curve, sensitivity, specificity and negative/positive predictive value, that about 3900 images were minimally required to demonstrate an acceptable performance, and that algorithm performance can be customized according to the population characteristics by adjusting threshold value.

Usage of the Internet of Things in Medical Institutions and its Implications

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.

Automation of generative adversarial network-based synthetic data-augmentation for maximizing the diagnostic performance with paranasal imaging

Thus far, there have been no reported specific rules for systematically determining the appropriate augmented sample size to optimize model performance when conducting data augmentation. In this paper, we report on the feasibility of synthetic data augmentation using generative adversarial networks (GAN) by proposing an automation pipeline to find the optimal multiple of data augmentation to achieve the best deep learning-based diagnostic performance in a limited dataset. We used Waters' view radiographs for patients diagnosed with chronic sinusitis to demonstrate the method developed herein. We demonstrate that our approach produces significantly better diagnostic performance parameters than models trained using conventional data augmentation. The deep learning method proposed in this study could be implemented to assist radiologists in improving their diagnosis. Researchers and industry workers could overcome the lack of training data by employing our proposed automation pipeline approach in GAN-based synthetic data augmentation. This is anticipated to provide new means to overcome the shortage of graphic data for algorithm training.

Review of Smart Hospital Services in Real Healthcare Environments

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.

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.

Colonoscopic image synthesis with generative adversarial network for enhanced detection of sessile serrated lesions using convolutional neural network

Computer-aided detection (CADe) systems have been actively researched for polyp detection in colonoscopy. To be an effective system, it is important to detect additional polyps that may be easily missed by endoscopists. Sessile serrated lesions (SSLs) are a precursor to colorectal cancer with a relatively higher miss rate, owing to their flat and subtle morphology. Colonoscopy CADe systems could help endoscopists; however, the current systems exhibit a very low performance for detecting SSLs. We propose a polyp detection system that reflects the morphological characteristics of SSLs to detect unrecognized or easily missed polyps. To develop a well-trained system with imbalanced polyp data, a generative adversarial network (GAN) was used to synthesize high-resolution whole endoscopic images, including SSL. Quantitative and qualitative evaluations on GAN-synthesized images ensure that synthetic images are realistic and include SSL endoscopic features. Moreover, traditional augmentation methods were used to compare the efficacy of the GAN augmentation method. The CADe system augmented with GAN synthesized images showed a 17.5% improvement in sensitivity on SSLs. Consequently, we verified the potential of the GAN to synthesize high-resolution images with endoscopic features and the proposed system was found to be effective in detecting easily missed polyps during a colonoscopy.

Companion robots for older adults: Rodgers' evolutionary concept analysis approach

This study aims to analyze the concept of companion robots for older adults from the perspective of nursing. This study employed a concept analysis. The literature from July 2011 to June 2021 was sought from databases using specific keywords. Any quantitative or qualitative study published in English or Korean focusing on companion robots for older adults was included in the study. Rodgers' evolutionary concept analysis was used to clarify the antecedents, attributes, and consequences. Seventy-five eligible articles were studied. The findings were categorized into antecedents, attributes, and consequences. Companion robot antecedents were classified into individual factors, attitude toward robots, and caregiver and social factors. The defining attributes included human-robot interaction, function, features, structure, cost, and management of the robot being a companion. Consequences were categorized into user, caregiver, and health related. Companion robots are designed to enhance well-being, quality of life, and independence by providing service and companionship and assisting daily life. This mainly includes cognitive and social support, mobility support, relaxation, health monitoring, and self-care support through human-robot interaction. The attributes, antecedents, and consequences of companion robots identified in this study can inform future decision making and interventions by caregivers for aging in place.

[Influence of female age on the fresh cycle live birth rate of different controlled ovarian hyperstimulation protocols in poor ovarian response patients]

Objective: To investigate the influence of age on the fresh cycle live birth rate in patients with poor ovarian response in different controlled ovarian hyperstimulation groups. Methods: The clinical data of 3 342 patients in The First Affiliated Hospital of Zhengzhou University from February 2014 to November 2018 were retrospectively collected, including early-follicular phase long-acting gonadotropin-releasing hormone (GnRH) agonist long protocol group (1 375 cases), mid-luteal phase short-acting GnRH agonist long protocol group (1 161 cases) and GnRH antagonist protocol group (806 cases); each group was divided into 4 subgroups according to age: ≤30 years, 31－35 years, 36－40 years and >40 years, the pregnancy outcomes in each age subgroup were analyzed under different controlled ovarian hyperstimulation protocols. Results: In early-follicular phase long-acting GnRH agonist long protocol group, the final live birth rates of each age subgroup were 39.4% (228/579), 36.1% (135/374), 16.6% (48/290) and 3.0% (4/132); in mid-luteal phase short-acting GnRH agonist long protocol group, live birth rates of each age subgroup were 32.1% (99/308), 20.8% (55/264), 13.0% (45/346) and 7.0% (17/243); in GnRH antagonist protocol group, live birth rates of each age subgroup were 22.8% (26/114), 16.3% (25/153), 11.2% (31/278), and 3.8% (10/261); the live birth rate of each group decreased significantly with the increase of age (all P<0.01). When the age≤35 years old, the fresh cycle live birth rate of the early-follicular phase long-acting GnRH agonist long protocol group was significantly better than those of the other two groups (all P<0.01). The multivariate logistic regression analysis of age and live birth rate of the three controlled ovarian hyperstimulation groups showed age was the independent influence factor (OR=0.898, 95%CI: 0.873-0.916, P<0.01; OR=0.926, 95%CI: 0.890-0.996, P<0.01; OR=0.901, 95%CI: 0.863-0.960, P<0.01). Conclusions: Age is an independent influencing factor for the prediction of fresh cycle live birth rate in low ovarian response patients. No matter which controlled ovarian hyperstimulation protocol is adopted, the final live birth rate decreases significantly with the increase of women's age. In addition, the early-follicular phase long-acting GnRH agonist long protocol has the highest fresh cycle live birth rate among all controlled ovarian hyperstimulation groups.

Machine learning-based preoperative datamining can predict the therapeutic outcome of sleep surgery in OSA subjects

Increasing recognition of anatomical obstruction has resulted in a large variety of sleep surgeries to improve anatomic collapse of obstructive sleep apnea (OSA) and the prediction of whether sleep surgery will have successful outcome is very important. The aim of this study is to assess a machine learning-based clinical model that predict the success rate of sleep surgery in OSA subjects. The predicted success rate from machine learning and the predicted subjective surgical outcome from the physician were compared with the actual success rate in 163 male dominated-OSA subjects. Predicted success rate of sleep surgery from machine learning models based on sleep parameters and endoscopic findings of upper airway demonstrated higher accuracy than subjective predicted value of sleep surgeon. The gradient boosting model showed the best performance to predict the surgical success that is evaluated by pre- and post-operative polysomnography or home sleep apnea testing among the logistic regression and three machine learning models, and the accuracy of gradient boosting model (0.708) was significantly higher than logistic regression model (0.542). Our data demonstrate that the data mining-driven prediction such as gradient boosting exhibited higher accuracy for prediction of surgical outcome and we can provide accurate information on surgical outcomes before surgery to OSA subjects using machine learning models.

Federated Learning for Thyroid Ultrasound Image Analysis to Protect Personal Information: Validation Study in a Real Health Care Environment

Background

Federated learning is a decentralized approach to machine learning; it is a training strategy that overcomes medical data privacy regulations and generalizes deep learning algorithms. Federated learning mitigates many systemic privacy risks by sharing only the model and parameters for training, without the need to export existing medical data sets. In this study, we performed ultrasound image analysis using federated learning to predict whether thyroid nodules were benign or malignant.

Objective

The goal of this study was to evaluate whether the performance of federated learning was comparable with that of conventional deep learning.

Methods

A total of 8457 (5375 malignant, 3082 benign) ultrasound images were collected from 6 institutions and used for federated learning and conventional deep learning. Five deep learning networks (VGG19, ResNet50, ResNext50, SE-ResNet50, and SE-ResNext50) were used. Using stratified random sampling, we selected 20% (1075 malignant, 616 benign) of the total images for internal validation. For external validation, we used 100 ultrasound images (50 malignant, 50 benign) from another institution.

Results

For internal validation, the area under the receiver operating characteristic (AUROC) curve for federated learning was between 78.88% and 87.56%, and the AUROC for conventional deep learning was between 82.61% and 91.57%. For external validation, the AUROC for federated learning was between 75.20% and 86.72%, and the AUROC curve for conventional deep learning was between 73.04% and 91.04%.

Conclusions

We demonstrated that the performance of federated learning using decentralized data was comparable to that of conventional deep learning using pooled data. Federated learning might be potentially useful for analyzing medical images while protecting patients’ personal information.

Quantitative assessment of self-treated canalith repositioning procedures using inertial measurement unit sensors

BACKGROUND

Low success and high recurrence of benign paroxysmal positional vertigo (BPPV) after home-based self-treated Epley and Barbeque (BBQ) roll maneuvers is an important issue.

OBJECTIVE

To quantify the cause of low success rate of self-treated Epley and BBQ roll maneuvers and provide a clinically acceptable criterion to guide self-treatment head rotations.

METHODS

Twenty-five participants without active BPPV wore a custom head-mount rotation monitoring device for objective measurements. Self-treatment and specialist-assisted maneuvers were compared for head rotation accuracy. Absolute differences between the head rotation evaluation criteria (American Academy of Otolaryngology guidelines) and measured rotation angles were considered as errors. Self-treatment and specialist-treated errors in maneuvers were compared. Between-trial variations and age effects were evaluated.

RESULTS

A significantly large error and between-trial variation occurred in step 4 of the self-treated Epley maneuver, with a considerable error in the second trial. The cumulative error of all steps of self-treated BBQ roll maneuver was significantly large. Age effect occurred only in the self-treated BBQ roll maneuver. Errors in specialist-treated maneuvers ranged from 10 to 20 degrees.

CONCLUSIONS

Real-time feedback of head movements during simultaneous head-body rotations could increase success rates of self-treatments. Specialist-treated maneuvers can be used as permissible rotation margin criteria.

Codeless Deep Learning of COVID-19 Chest X-Ray Image Dataset with KNIME Analytics Platform

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.

Usability Evaluation of User Requirement-Based Teleconsultation Robots: A Preliminary Report from South Korea

BACKGROUND

Telepresence robots used to deliver a point-of-care (POC) consultation system that may provide value to enable effective decision making by healthcare providers at care sites.

OBJECTIVES

This study aimed to evaluate usability of teleconsultation robots, based on endusers' needs, that can improve acceptance in future robot applications.

METHODS

This is a single group postdesign study using mixed methods to assess the usability of teleconsultation robots using scenarios. To collect opinions from various departments, 15 nurses or physicians currently working at medical institutions in Korea were selected using purposive sampling. The usability evaluation was conducted on healthcare providers twice at the simulation center; the think-aloud method was used and surveys and interviews were conducted to identify problems or improvements that may arise from the use of robots in hospital settings.

RESULTS

The results showed that perceived usefulness, perceived ease of use, and satisfaction level each scored 4 points or higher out of 7 points, showing usability of midhigh level. Camera angle control and robot driving functions were the most difficult. Other basic robot user interface was shown to be relatively easy. There was no difference in usability depending on the characteristics of the evaluator. Some functions including user interface were modified based on the usability test.

CONCLUSION

Using robots in health care institutions may support effective communication among healthcare providers, thus contributing to health care improvement.

Vision-based tracking system for augmented reality to localize recurrent laryngeal nerve during robotic thyroid surgery

We adopted a vision-based tracking system for augmented reality (AR), and evaluated whether it helped surgeons to localize the recurrent laryngeal nerve (RLN) during robotic thyroid surgery. We constructed an AR image of the trachea, common carotid artery, and RLN using CT images. During surgery, an AR image of the trachea and common carotid artery were overlaid on the physical structures after they were exposed. The vision-based tracking system was activated so that the AR image of the RLN followed the camera movement. After identifying the RLN, the distance between the AR image of the RLN and the actual RLN was measured. Eleven RLNs (9 right, 4 left) were tested. The mean distance between the RLN AR image and the actual RLN was 1.9 ± 1.5 mm (range 0.5 to 3.7). RLN localization using AR and vision-based tracking system was successfully applied during robotic thyroidectomy. There were no cases of RLN palsy. This technique may allow surgeons to identify hidden anatomical structures during robotic surgery.

Ratiometric Impedance Sensing of Fingers for Robust Identity Authentication

We present a novel biometric authentication system enabled by ratiometric analysis of impedance of fingers. In comparison to the traditional biometrics that relies on acquired images of structural information of physiological characteristics, our biological impedance approach not only eliminates any practical means of making fake copies of the relevant physiological traits but also provides reliable features of biometrics using the ratiometric impedance of fingers. This study shows that the ratiometric features of the impedance of fingers in 10 different pairs using 5 electrodes at the fingertips can reduce the variation due to undesirable factors such as temperature and day-to-day physiological variations. By calculating the ratio of impedances, the difference between individual subjects was amplified and the spectral patterns were diversified. Overall, our ratiometric analysis of impedance improved the classification accuracy of 41 subjects and reduced the error rate of classification from 29.32% to 5.86% (by a factor of 5).

Identification of cecum time-location in a colonoscopy video by deep learning analysis of colonoscope movement

Background

Cecal intubation time is an important component for quality colonoscopy. Cecum is the turning point that determines the insertion and withdrawal phase of the colonoscope. For this reason, obtaining information related with location of the cecum in the endoscopic procedure is very useful. Also, it is necessary to detect the direction of colonoscope's movement and time-location of the cecum.

Methods

In order to analysis the direction of scope's movement, the Horn-Schunck algorithm was used to compute the pixel's motion change between consecutive frames. Horn-Schunk-algorithm applied images were trained and tested through convolutional neural network deep learning methods, and classified to the insertion, withdrawal and stop movements. Based on the scope's movement, the graph was drawn with a value of +1 for insertion, -1 for withdrawal, and 0 for stop. We regarded the turning point as a cecum candidate point when the total graph area sum in a certain section recorded the lowest.

Results

A total of 328,927 frame images were obtained from 112 patients. The overall accuracy, drawn from 5-fold cross-validation, was 95.6%. When the value of "t" was 30 s, accuracy of cecum discovery was 96.7%. In order to increase visibility, the movement of the scope was added to summary report of colonoscopy video. Insertion, withdrawal, and stop movements were mapped to each color and expressed with various scale. As the scale increased, the distinction between the insertion phase and the withdrawal phase became clearer.

Conclusion

Information obtained in this study can be utilized as metadata for proficiency assessment. Since insertion and withdrawal are technically different movements, data of scope's movement and phase can be quantified and utilized to express pattern unique to the colonoscopist and to assess proficiency. Also, we hope that the findings of this study can contribute to the informatics field of medical records so that medical charts can be transmitted graphically and effectively in the field of colonoscopy.

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.

Development of Artificial Intelligence to Support Needle Electromyography Diagnostic Analysis

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.

Ultrasound image analysis using deep learning algorithm for the diagnosis of thyroid nodules

Fine needle aspiration (FNA) is the procedure of choice for evaluating thyroid nodules. It is indicated for nodules >2 cm, even in cases of very low suspicion of malignancy. FNA has associated risks and expenses. In this study, we developed an image analysis model using a deep learning algorithm and evaluated if the algorithm could predict thyroid nodules with benign FNA results.Ultrasonographic images of thyroid nodules with cytologic or histologic results were retrospectively collected. For algorithm training, 1358 (670 benign, 688 malignant) thyroid nodule images were input into the Inception-V3 network model. The model was pretrained to classify nodules as benign or malignant using the ImageNet database. The diagnostic performance of the algorithm was tested with the prospectively collected internal (n = 55) and external test sets (n = 100).For the internal test set, 20 of the 21 FNA malignant nodules were correctly classified as malignant by the algorithm (sensitivity, 95.2%); and of the 22 nodules algorithm classified as benign, 21 were FNA benign (negative predictive value [NPV], 95.5%). For the external test set, 47 of the 50 FNA malignant nodules were correctly classified by the algorithm (sensitivity, 94.0%); and of the 31 nodules the algorithm classified as benign, 28 were FNA benign (NPV, 90.3%).The sensitivity and NPV of the deep learning algorithm shown in this study are promising. Artificial intelligence may assist clinicians to recognize nodules that are likely to be benign and avoid unnecessary FNA.

Preliminary study on application of augmented reality visualization in robotic thyroid surgery

Purpose

Increased robotic surgery is attended by increased reports of complications, largely due to limited operative view and lack of tactile sense. These kinds of obstacles, which seldom occur in open surgery, are challenging for beginner surgeons. To enhance robotic surgery safety, we created an augmented reality (AR) model of the organs around the thyroid glands, and tested the AR model applicability in robotic thyroidectomy.

Methods

We created AR images of the thyroid gland, common carotid arteries, trachea, and esophagus using preoperative CT images of a thyroid carcinoma patient. For a preliminary test, we overlaid the AR images on a 3-dimensional printed model at five different angles and evaluated its accuracy using Dice similarity coefficient. We then overlaid the AR images on the real-time operative images during robotic thyroidectomy.

Results

The Dice similarity coefficients ranged from 0.984 to 0.9908, and the mean of the five different angles was 0.987. During the entire process of robotic thyroidectomy, the AR images were successfully overlaid on the real-time operative images using manual registration.

Conclusion

We successfully demonstrated the use of AR on the operative field during robotic thyroidectomy. Although there are currently limitations, the use of AR in robotic surgery will become more practical as the technology advances and may contribute to the enhancement of surgical safety.

High-Frequency Ultrasound Imaging for Examination of Early Dental Caries

The extent of dental tissue destruction during the treatment of white spot lesions (WSLs) increases with the severity of the lesion. If the depth and shape of WSLs can be predicted with a noninvasive diagnostic method before dental caries treatment, more conservative interventions can be planned. Given the superiority of high-frequency ultrasound (HFUS) imaging in observing the internal structures of the body, the present study aimed to verify the possibility of HFUS imaging to examine the depth and shape of WSLs. We prepared tooth samples and developed a biomicroscopic system with a HFUS transducer to obtain images of normal and WSL regions. HFUS images were compared with conventional ultrasound images and micro-computed tomography images. HFUS distinctly differentiated demineralization within WSL and normal regions. WSL depth calculated in the micro-computed tomography image was similar to that in HFUS. This study revealed that HFUS imaging has the potential to detect early dental caries and offer information on the invasion depth of early dental caries quantitatively.

Augmented Reality to Localize Individual Organ in Surgical Procedure

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.

Web-Based Telepresence Exercise Program for Community-Dwelling Elderly Women With a High Risk of Falling: Randomized Controlled Trial

Background

While physical exercise is known to help prevent falls in the elderly, bad weather and long distance between the home and place of exercise represent substantial deterrents for the elderly to join or continue attending exercise programs outside their residence. Conventional modalities for home exercise can be helpful but do not offer direct and prompt feedback to the participant, which minimizes the benefit.

Objective

We aimed to develop an elderly-friendly telepresence exercise platform and to evaluate the effects of a 12-week telepresence exercise program on fall-related risk factors in community-dwelling elderly women with a high risk of falling.

Methods

In total, 34 women aged 68-91 years with Fall Risk Assessment scores >14 and no medical contraindication to physical training-based therapy were recruited in person from a senior citizen center. The telepresence exercise platform included a 15-inch tablet computer, custom-made peer-to-peer video conferencing server system, and broadband Internet connectivity. The Web-based program included supervised resistance exercises performed using elastic resistance bands and balance exercise for 20-40 minutes a day, three times a week, for 12 weeks. During the telepresence exercise session, each participant in the intervention group was supervised remotely by a specialized instructor who provided feedback in real time. The women in the control group maintained their lifestyle without any intervention. Fall-related physical factors (body composition and physical function parameters) and psychological factors (Korean Falls Efficacy Scale score, Fear of Falling Questionnaire score) before and after the 12-week interventional period were examined in person by an exercise specialist blinded to the group allocation scheme.

Results

Of the 30 women enrolled, 23 completed the study. Compared to women in the control group (n=13), those in the intervention group (n=10) showed significant improvements on the scores for the chair stand test (95% confidence interval -10.45 to -5.94, P<.001), Berg Balance Scale (95% confidence interval -2.31 to -0.28, P=.02), and Fear of Falling Questionnaire (95% confidence interval 0.69-3.5, P=.01).

Conclusions

The telepresence exercise program had positive effects on fall-related risk factors in community-dwelling elderly women with a high risk of falling. Elderly-friendly telepresence technology for home-based exercises can serve as an effective intervention to improve fall-related physical and psychological factors.

Trial Registration

Clinical Research Information Service KCT0002710; https://cris.nih.go.kr/cris/en/search/ search_result_st01.jsp?seq=11246 (Archived by WebCite at http://www.webcitation.org/6zdSUEsmb)

Customer Discovery as the First Essential Step for Successful Health Information Technology System Development

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.

White blood cell differential count of maturation stages in bone marrow smear using dual-stage convolutional neural networks

The white blood cell differential count of the bone marrow provides information concerning the distribution of immature and mature cells within maturation stages. The results of such examinations are important for the diagnosis of various diseases and for follow-up care after chemotherapy. However, manual, labor-intensive methods to determine the differential count lead to inter- and intra-variations among the results obtained by hematologists. Therefore, an automated system to conduct the white blood cell differential count is highly desirable, but several difficulties hinder progress. There are variations in the white blood cells of each maturation stage, small inter-class differences within each stage, and variations in images because of the different acquisition and staining processes. Moreover, a large number of classes need to be classified for bone marrow smear analysis, and the high density of touching cells in bone marrow smears renders difficult the segmentation of single cells, which is crucial to traditional image processing and machine learning. Few studies have attempted to discriminate bone marrow cells, and even these have either discriminated only a few classes or yielded insufficient performance. In this study, we propose an automated white blood cell differential counting system from bone marrow smear images using a dual-stage convolutional neural network (CNN). A total of 2,174 patch images were collected for training and testing. The dual-stage CNN classified images into 10 classes of the myeloid and erythroid maturation series, and achieved an accuracy of 97.06%, a precision of 97.13%, a recall of 97.06%, and an F-1 score of 97.1%. The proposed method not only showed high classification performance, but also successfully classified raw images without single cell segmentation and manual feature extraction by implementing CNN. Moreover, it demonstrated rotation and location invariance. These results highlight the promise of the proposed method as an automated white blood cell differential count system.

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.

Characterization of novel microsatellite markers derived from Korean rose bitterling (Rhodeus uyekii) genomic library

Korean rose bitterling (Rhodeus uyekii) is a freshwater fish endemic to Korea. Natural populations of this species have experienced severe declines as a result of habitat fragmentation and water pollution. To conserve and restore R. uyekii, the genetic diversity of this species needs to be assessed at the population level. Eighteen novel polymorphic microsatellite loci for R. uyekii were developed using an enriched partial genomic library. Polymorphisms at these loci were studied in 150 individuals collected from three populations. The number of alleles at each locus ranged from 3 to 47 (mean = 17.1). Within the populations, the observed heterozygosity ranged from 0.032 to 1.000, expected heterozygosity from 0.082 to 0.967, and polymorphism information content from 0.078 to 0.950. Six loci showed significant deviation from Hardy-Weinberg equilibrium after Bonferroni's correction, and no significant linkage disequilibrium was detected between most locus pairs, except in three cases. These highly informative microsatellite markers should be useful for genetic population structure analyses of R. uyekii.

Trends in the incidence of and survival rates for oral cavity cancer in the Korean population

OBJECTIVE

This study assessed trends in the incidence of and survival rates for oral cavity cancer in the Korean population.

MATERIALS AND METHODS

Data from the Korea Central Cancer Registry were extracted for 10,282 patients diagnosed with oral cavity cancer (C01-C06) between 1999 and 2010 to evaluate the age-standardised incidence rate, annual percentage change (APC) and 5-year relative survival rate (RSR) according to gender and age.

RESULTS

In males, the incidence rate slightly decreased [APC of -0.2% (P = 0.6427)]; in females, the incidence rate increased [APC of 3.1% (P < 0.05)]. In males and females, the incidence of oral tongue cancer (C02) significantly increased [APC of 2.2% and 4.1%, respectively (P < 0.05)]. This increase in oral tongue cancer incidence was most prominent in the younger age group (<40 years, APC = 6.1%, P < 0.05). The incidence of buccal cheek cancer increased only among males [APC of 4.8% (P < 0.05)]. The 5-year RSR improved from 42.7% (1993-1995) to 59.5% (2006-2010), corresponding to an increase of 16.8% from 1993 to 2010 (P < 0.05).

CONCLUSION

The incidence of oral cavity cancer in females increased, whereas it stabilised or decreased in males. However, the incidence of oral tongue cancer increased in both males and females, especially in the younger age group.

Initial design method based on an iterative calculation of aberration and its application to an objective lens for imaging spectrometer

An initial optical design method based on an iterative calculation of third-order aberration is presented to overcome the problems of the conventional method. The aberrations of each lens group in the optical system are calculated individually and iteratively under the constraint that aberrations of one group compensate for those of the other groups. The stabilities of initial design results have been confirmed and the iterative design method has been applied for the design of optical system with an external entrance pupil for imaging spectrometer. The designed lens corresponds to an objective lens with the aperture of F/1.5 and the focal length of 30 mm.

3D fabrication of biological machines

Cell-based biological machines can be defined as a set of sub-components consisting of living cells and cell-instructive micro-environments that interact to perform a range of prescribed tasks. The realization of biological machines and their sub-components will require a number of suitable cell sources, biomaterials, and enabling technologies. Here, we review our group's recent accomplishments and continuing efforts toward the development of building biological machines.

Complex fractionated electrograms and AF nests in vagally mediated atrial fibrillation

BACKGROUND

Catheter ablation targeting of complex fractionated atrial electrograms (CFAEs) is one of the techniques used for atrial fibrillation (AF) ablation. The ablation of sites showing a high-frequency spectral component (HFC) during sinus rhythm, known as AF nests, has been introduced as an adjunct to conventional ablation. Known locations of some AF nests are similar to CFAE sites. However, it has not been systematically evaluated whether these two targets represent the same foci. The purpose of this study was to compare the anatomical locations of these sites using an animal model of vagally mediated AF.

METHODS

Five anesthetized open-chest dogs were evaluated. Atrial electrograms were obtained epicardially. AF was induced by burst atrial pacing with 20 Hz during vagal stimulation. A total of 15 sites (eight sites in right atrium and seven sites in left atrium) were evaluated in each animal. The CFAE was determined during AF according to the electrogram patterns. After sinus conversion, real-time spectrum analysis was used for AF nest assessment at the same location.

RESULTS

The CFAE was observed at the high and mid sulcus terminalis areas, pulmonary vein antrum, and mid portion of the coronary sinus. Among them, only 60% of the CFAE sites showed HFC during sinus rhythm. In addition, some of the non-CFAE sites (22%) showed HFC during sinus rhythm.

CONCLUSION

The CFAE sites were not the same as the AF nests in this animal model of vagally mediated AF. Therefore, these two types of ablation methods appear to target different substrates of AF.