Simultaneous Viability Assessment and Invasive Coronary Angiography Using a Therapeutic CT System in Chronic Myocardial Infarction Patients.

PURPOSE: In a preclinical study using a swine myocardial infarction (MI) model, a delayed enhancement (DE)-multi-detector computed tomography (MDCT) scan was performed using a hybrid system alongside diagnostic invasive coronary angiography (ICA) without the additional use of a contrast agent, and demonstrated an excellent correlation in the infarct area compared with histopathologic specimens. In the present investigation, we evaluated the feasibility and diagnostic accuracy of a myocardial viability assessment by DE-MDCT using a hybrid system comprising ICA and MDCT alongside diagnostic ICA without the additional use of a contrast agent. MATERIALS AND METHODS: We prospectively enrolled 13 patients (median age: 67 years) with a previous MI (>6 months) scheduled to undergo ICA. All patients underwent cardiac magnetic resonance (CMR) imaging before diagnostic ICA. MDCT viability scans were performed concurrently with diagnostic ICA without the use of additional contrast. The total myocardial scar volume per patient and average transmurality per myocardial segment measured by DE-MDCT were compared with those from DE-CMR. RESULTS: The DE volume measured by MDCT showed an excellent correlation with the volume measured by CMR (r=0.986, p<0.0001). The transmurality per segment by MDCT was well-correlated with CMR (r=0.900, p<0.0001); the diagnostic performance of MDCT in differentiating non-viable from viable myocardium using a 50% transmurality criterion was good with a sensitivity, specificity, positive predictive value, negative predictive value, and accuracy of 87.5%, 99.5%, 87.5%, 99.5%, and 99.1%, respectively. CONCLUSION: The feasibility of the DE-MDCT viability assessment acquired simultaneously with conventional ICA was proven in patients with chronic MI using DE-CMR as the reference standard.

A Study on the Biofilm Removal Efficacy of a Bioelectric Toothbrush.

Effective oral care is a critical requirement to maintain a high quality of life. Most oral diseases are caused by plaque (oral biofilm), which is also correlated with systemic diseases. A common method to remove biofilm is brushing teeth with toothpaste. However, 3.5 billion people in the world have oral diseases, meaning that more efficient methods of removing biofilms are needed. We have developed a toothbrush that applies a bioelectric effect (BE) utilizing an electric force for biofilm removal. It demonstrated significantly higher biofilm removal efficiency than non-BE manual toothbrushes. Tests were performed in saline and toothpaste conditions using various pressures. Results showed that the BE toothbrush had a significantly higher biofilm removal efficiency in saline (0.5 N: 215.43 ± 89.92%, 2.5 N: 116.77 ± 47.02%) and in a toothpaste slurry (0.5 N: 104.96 ± 98.93%, 2.5 N: 96.23 ± 35.16%) than non-BE manual toothbrushes. Results also showed that BE toothbrushes were less dependent on toothpaste. This study suggests that the application of BE can be a new solution to plaque problems in oral care.

Development and Validation of an Artificial Intelligence-Based Motion Analysis System for Upper Extremity Rehabilitation Exercises in Patients with Spinal Cord Injury: A Randomized Controlled Trial.

In this study, we developed an AI-based real-time motion feedback system for patients with spinal cord injury (SCI) during rehabilitation, aiming to enhance their interest and motivation. The effectiveness of the system in improving upper-limb muscle strength during the Thera band exercises was evaluated. The motion analysis program, including exercise repetition counts and calorie consumption, was developed using MediaPipe, focusing on three key motions (chest press, shoulder press, and arm curl) for upper extremity exercises. The participants with SCI were randomly assigned to the experimental group (EG = 4) or control group (CG = 5), engaging in 1 h sessions three times a week for 8 weeks. Muscle strength tests (chest press, shoulder press, lat pull-down, and arm curl) were performed before and after exercises. Although both groups did not show significant differences, the EG group exhibited increased strength in all measured variables, whereas the CG group showed constant or reduced results. Consequently, the computer program-based system developed in this study could be effective in muscle strengthening. Furthermore, these findings may serve as a valuable foundation for future AI-driven rehabilitation exercise systems.

Fully automated quantification of cardiac chamber and function assessment in 2-D echocardiography: clinical feasibility of deep learning-based algorithms.

We aimed to compare the segmentation performance of the current prominent deep learning (DL) algorithms with ground-truth segmentations and to validate the reproducibility of the manually created 2D echocardiographic four cardiac chamber ground-truth annotation. Recently emerged DL based fully-automated chamber segmentation and function assessment methods have shown great potential for future application in aiding image acquisition, quantification, and suggestion for diagnosis. However, the performance of current DL algorithms have not previously been compared with each other. In addition, the reproducibility of ground-truth annotations which are the basis of these algorithms have not yet been fully validated. We retrospectively enrolled 500 consecutive patients who underwent transthoracic echocardiogram (TTE) from December 2019 to December 2020. Simple U-net, Res-U-net, and Dense-U-net algorithms were compared for the segmentation performances and clinical indices such as left atrial volume (LAV), left ventricular end diastolic volume (LVEDV), left ventricular end systolic volume (LVESV), LV mass, and ejection fraction (EF) were evaluated. The inter- and intra-observer variability analysis was performed by two expert sonographers for a randomly selected echocardiographic view in 100 patients (apical 2-chamber, apical 4-chamber, and parasternal short axis views). The overall performance of all DL methods was excellent [average dice similarity coefficient (DSC) 0.91 to 0.95 and average Intersection over union (IOU) 0.83 to 0.90], with the exception of LV wall area on PSAX view (average DSC of 0.83, IOU 0.72). In addition, there were no significant difference in clinical indices between ground truth and automated DL measurements. For inter- and intra-observer variability analysis, the overall intra observer reproducibility was excellent: LAV (ICC = 0.995), LVEDV (ICC = 0.996), LVESV (ICC = 0.997), LV mass (ICC = 0.991) and EF (ICC = 0.984). The inter-observer reproducibility was slightly lower as compared to intraobserver agreement: LAV (ICC = 0.976), LVEDV (ICC = 0.982), LVESV (ICC = 0.970), LV mass (ICC = 0.971), and EF (ICC = 0.899). The three current prominent DL-based fully automated methods are able to reliably perform four-chamber segmentation and quantification of clinical indices. Furthermore, we were able to validate the four cardiac chamber ground-truth annotation and demonstrate an overall excellent reproducibility, but still with some degree of inter-observer variability.

In vitro delivery efficiencies of nebulizers for different breathing patterns.

BACKGROUND: Nebulizers are medical devices that deliver aerosolized medication directly to lungs to treat a variety of respiratory diseases. However, breathing patterns, respiration rates, airway diameters, and amounts of drugs delivered by nebulizers may be respiratory disease dependent. METHOD: In this study, we developed a respiratory simulator consisting of an airway model, an artificial lung, a flow sensor, and an aerosol collecting filter. Various breathing patterns were generated using a linear actuator and an air cylinder. We tested six home nebulizers (jet (2), static (2), and vibrating mesh nebulizers (2)). Nebulizers were evaluated under two conditions, that is, for the duration of nebulization and at a constant output 1.3 mL using four breathing patterns, namely, the breathing pattern specified in ISO 27427:2013, normal adult, asthmatic, and COPD. RESULTS: One of the vibrating mesh nebulizers had the highest dose delivery efficiency. The drug delivery efficiencies of nebulizers were found to depend on breathing patterns. CONCLUSION: We suggest a quantitative drug delivery efficiency evaluation method and calculation parameters that include considerations of constant outputs and residual volumes. The study shows output rates and breathing patterns should be considered when the drug delivery efficiencies of nebulizers are evaluated.

Postganglionic Sudomotor Dysfunction and Brain Glucose Hypometabolism in Patients with Multiple System Atrophy.

BACKGROUND: Sudomotor dysfunction is common in patients with multiple system atrophy (MSA). Postganglionic sudomotor dysfunction in MSA, which can be assessed using quantitative sudomotor axon reflex testing (QSART), results from the degeneration of preganglionic sympathetic neurons and direct loss of postganglionic fibers. OBJECTIVE: We investigate whether abnormal QSART responses in patients with MSA are associated with disease severity. METHODS: In this retrospective study, patients with probable MSA who underwent both 18F-fluorodeoxyglucose positron emission tomography/computed tomography (18F-FDG-PET/CT) and autonomic function tests were included. Autonomic function test results were integrated divided into three sub-scores, including sudomotor, cardiovagal, and adrenergic sub-scores. The sudomotor sub-score represented postganglionic sudomotor function. Unified Multiple System Atrophy Rating Scale (UMSARS) Part I, Part II, and sum of Part I and II scores (Part I + II) to reflect disease severity and 18F-FDG-PET/CT results were collected. RESULTS: Of 74 patients with MSA, 62.2%demonstrated abnormal QSART results. The UMSARS Part I + II score was significantly higher in the abnormal QSART group than in the normal QSART group (p = 0.037). In the regression analysis, both UMSARS Part I (ß= 1.185, p = 0.013) and Part II (ß= 1.266, p = 0.021) scores were significantly associated with the sudomotor sub-score. On 18F-FDG-PET/CT, the abnormal QSART group exhibited more severely decreased metabolic activity in the cerebellum and basal ganglia in patients with MSA-P and MSA-C, respectively. The sudomotor sub-score was significantly associated with regional metabolism in these areas. CONCLUSION: Patients with MSA and postganglionic sudomotor dysfunction may have worse disease severity and greater neuropathological burden than those without.

Segmentation of white matter hyperintensities on 18F-FDG PET/CT images with a generative adversarial network.

PURPOSE: White matter hyperintensities (WMH) are typically segmented using MRI because WMH are hardly visible on 18F-FDG PET/CT. This retrospective study was conducted to segment WMH and estimate their volumes from 18F-FDG PET with a generative adversarial network (WhyperGAN). METHODS: We selected patients whose interval between MRI and FDG PET/CT scans was within 3 months, from January 2017 to December 2018, and classified them into mild, moderate, and severe groups by following the semiquantitative rating method of Fazekas. For each group, 50 patients were selected, and of them, we randomly selected 35 patients for training and 15 for testing. WMH were automatically segmented from FLAIR MRI with manual adjustment. Patches of WMH were extracted from 18F-FDG PET and segmented MRI. WhyperGAN was compared with H-DenseUnet, a deep learning method widely used for segmentation tasks, for segmentation performance based on the dice similarity coefficient (DSC), recall, and average volume differences (AVD). For volume estimation, the predicted WMH volumes from PET were compared with ground truth volumes. RESULTS: The DSC values were associated with WMH volumes on MRI. For volumes >60 mL, the DSC values were 0.751 for WhyperGAN and 0.564 for H-DenseUnet. For volumes ≤60 mL, the DSC values rapidly decreased as the volume decreased (0.362 for WhyperGAN vs. 0.237 for H-DenseUnet). For recall, WhyperGAN achieved the highest value in the severe group (0.579 for WhyperGAN vs. 0.509 for H-DenseUnet). For AVD, WhyperGAN achieved the lowest score in the severe group (0.494 for WhyperGAN vs. 0.941 for H-DenseUnet). For the WMH volume estimation, WhyperGAN performed better than H-DenseUnet and yielded excellent correlation coefficients (r = 0.998, 0.983, and 0.908 in the severe, moderate, and mild group). CONCLUSIONS: Although limited by visual analysis, the WhyperGAN based can be used to automatically segment and estimate volumes of WMH from 18F-FDG PET/CT. This would increase the usefulness of 18F-FDG PET/CT for the evaluation of WMH in patients with cognitive impairment.

PET/CT for Brain Amyloid: A Feasibility Study for Scan Time Reduction by Deep Learning.

PURPOSE: This study was to develop a convolutional neural network (CNN) model with a residual learning framework to predict the full-time 18F-florbetaben (18F-FBB) PET/CT images from corresponding short-time scans. METHODS: In this retrospective study, we enrolled 22 cognitively normal subjects, 20 patients with mild cognitive impairment, and 42 patients with Alzheimer disease. Twenty minutes of list-mode PET/CT data were acquired and reconstructed as the ground-truth images. The short-time scans were made in either 1, 2, 3, 4, or 5 minutes. The CNN with a residual learning framework was implemented to predict the ground-truth images of 18F-FBB PET/CT using short-time scans with either a single-slice or a 3-slice input layer. Model performance was evaluated by quantitative and qualitative analyses. Additionally, we quantified the amyloid load in the ground-truth and predicted images using the SUV ratio. RESULTS: On quantitative analyses, with increasing scan time, the normalized root-mean-squared error and the SUV ratio differences between predicted and ground-truth images gradually decreased, and the peak signal-to-noise ratio increased. On qualitative analysis, the predicted images from the 3-slice CNN model showed better image quality than those from the single-slice model. The 3-slice CNN model with a short-time scan of at least 2 minutes achieved comparable, quantitative prediction of full-time 18F-FBB PET/CT images, with adequate to excellent image quality. CONCLUSIONS: The 3-slice CNN model with a residual learning framework is promising for the prediction of full-time 18F-FBB PET/CT images from short-time scans.

Aerosol Delivery of Dornase Alfa Generated by Jet and Mesh Nebulizers.

Recent reports on mesh nebulizers suggest the possibility of stable nebulization of various therapeutic protein drugs. In this study, the in vitro performance and drug stability of jet and mesh nebulizers were examined for dornase alfa and compared with respect to their lung delivery efficiency in BALB/c mice. We compared four nebulizers: two jet nebulizers (PARI BOY SX with red and blue nozzles), a static mesh nebulizer (NE-U150), and a vibrating mesh nebulizer (NE-SM1). The enzymatic activity of dornase alfa was assessed using a kinetic fluorometric DNase activity assay. Both jet nebulizers had large residual volumes between 24% and 27%, while the volume of the NE-SM1 nebulizer was less than 2%. Evaluation of dornase alfa aerosols produced by the four nebulizers showed no overall loss of enzymatic activity or protein content and no increase in aggregation or degradation. The amount of dornase alfa delivered to the lungs was highest for the PARI BOY SX-red jet nebulizer. This result confirmed that aerosol droplet size is an important factor in determining the efficiency of dornase alfa delivery to the lungs. Further clinical studies and analysis are required before any conclusions can be drawn regarding the clinical safety and efficacy of these nebulizers.

Slice-selective learning for Alzheimer's disease classification using a generative adversarial network: a feasibility study of external validation.

PURPOSE: The aim of this feasibility study was to use slice selective learning using a Generative Adversarial Network for external validation. We aimed to build a model less sensitive to PET imaging acquisition environment, since differences in environments negatively influence network performance. To investigate the slice performance, each slice evaluation was performed. METHODS: We trained our model using a 18F-fluorodeoxyglucose ([18F]FDG) PET/CT dataset obtained from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database and tested the model with a Severance Hospital dataset. We applied slice selective learning to reduce computational cost and to extract unbiased features. We extracted features of Alzheimer's disease (AD) and normal cognitive (NC) condition using a Boundary Equilibrium Generative Adversarial Network (BEGAN) for stable convergence. Then, we utilized these features to train a support vector machine (SVM) classifier to distinguish AD from NC. RESULTS: The slice range that covered the posterior cingulate cortex (PCC) using double slices showed the best performance. The accuracy, sensitivity, and specificity of our proposed network was 94.33%, 91.78%, and 97.06% using the Severance dataset and 94.82%, 92.11%, and 97.45% using the ADNI dataset. The performance on the two independent datasets showed no statistical difference (p > 0.05). Moreover, there was a statistical difference in the performance between using two slices and one slice as input (p < 0.05). CONCLUSIONS: Our model learned the generalized features of AD and NC for external validation when appropriate slices were selected. This study showed the feasibility of this model with consistent performance when tested using datasets acquired from a variety of image-acquisition environments.

Respiratory measurement using infrared thermography and respiratory volume monitor during sedation in patients undergoing endoscopic urologic procedures under spinal anesthesia.

We aimed to evaluate changes in respiratory pattern after sedation by simultaneously applying a respiratory volume monitor (ExSpiron1Xi, RVM) and infrared thermography (IRT) to patients undergoing spinal anesthesia during endoscopic urologic surgeries. After spinal anesthesia was performed, the patient was placed in a lithotomy position for surgery. Then, we established the baseline of the RVM, and started monitoring the mouth and nose with the infrared camera. SpO2 was continuously measured throughout these processes. Once the baseline was set, 0.05 mg/kg midazolam was administered for sedation. Apnea was defined as cessation of airflow for ≥ 10 s with respiratory rate of < 6 breaths/min; hypopnea was defined as a decrease in oxygen hemoglobin of > 4%, compared to baseline. We measured the time at which apnea was detected by IRT, the time at which hypopnea was detected by RVM, and the time at which hypoxia was detected by SpO2. Twenty patients (age: 68.9 ± 11.2 years, body mass index: 24.2 ± 2.6 kg/min2) completed the study. Before sedation, the baseline correlation coefficient of respiratory rate detection between RVM and IRT was 0.866. After midazolam administration, apnea was detected in all subjects within the first 5 min by IRT; the median time required to detect apnea was 102.5 [interquartile range (IQR) 25-75%: 80-155] s. Hypopnea was detected in all subjects within the first 5 min by RVM: the median time required to detect hypopnea was 142.5 (IQR 115-185.2) s. The median time required for SpO2 to decrease > 4% from baseline was 160 (IQR 125-205) s. Our results suggest that IRT can be useful for rapid detection of respiratory changes in patients undergoing sedation following spinal anesthesia for endoscopic urologic procedures.

The analgesic potency dose of remifentanil to minimize stress response induced by intubation and measurement uncertainty of Surgical Pleth Index.

BACKGROUND: The aim of this study was to evaluate the analgesic potency dose of remifentanil to maintain Surgical Pleth Index (SPI) values at less than 50 after intubation in patients undergoing general anesthesia with target-controlled infusion of propofol and remifentanil. METHODS: We randomly allocated 120 patients to receive one of three remifentanil target effect-site concentrations (5, 7, or 9 ng×mL-1) during intubation. The target effect-site concentrations of propofol were adjusted within a range of 2.5-3 µg×mL-1 to maintain bispectral index values at less than 60 during anesthesia induction. A reusable SPI sensor was placed on the index finger of the arm, and the SPI values were continuously recorded. The predicted probability for maintaining the SPI values at less than 50 after intubation against the cumulative amount of remifentanil was analyzed using logistic regression. The measurands were the baseline SPI value in patients without pain scheduled for surgery, and the maximal SPI value after intubation in patients receiving remifentanil with a target effect-site concentration of 7 ng×mL-1. RESULTS: The estimated cumulative amount of remifentanil associated with a 50% and 95% probability of maintaining the SPI values at less than 50 after intubation were 135.0 µg and 330.4 µg, respectively. The estimated expanded uncertainty for the baseline and maximal SPI values after intubation in patients scheduled for surgery were 54.9±44.4 and 54.1±37.9, respectively, which corresponded to a confidence level of approximately 95%. CONCLUSIONS: The analgesic potency dose of remifentanil to maintain SPI values at less than 50 after intubation was 135.0 µg.

Texture Analysis of Supraspinatus Ultrasound Image for Computer Aided Diagnostic System.

OBJECTIVES: In this paper, we proposed an algorithm for recognizing a rotator cuff supraspinatus tendon tear using a texture analysis based on a histogram, gray level co-occurrence matrix (GLCM), and gray level run length matrix (GLRLM). METHODS: First, we applied a total of 57 features (5 first order descriptors, 40 GLCM features, and 12 GLRLM features) to each rotator cuff region of interest. Our results show that first order statistics (mean, skewness, entropy, energy, smoothness), GLCM (correlation, contrast, energy, entropy, difference entropy, homogeneity, maximum probability, sum average, sum entropy), and GLRLM features are helpful to distinguish a normal supraspinatus tendon and an abnormal supraspinatus tendon. The statistical significance of these features is verified using a t-test. The support vector machine classification showed accuracy using feature combinations. Support Vector Machine offers good performance with a small amount of training data. Sensitivity, specificity, and accuracy are used to evaluate performance of a classification test. RESULTS: From the results, first order statics features and GLCM and GLRLM features afford 95%, 85%, and 100% accuracy, respectively. First order statistics and GLCM and GLRLM features in combination provided 100% accuracy. Combinations that include GLRLM features had high accuracy. GLRLM features were confirmed as highly accurate features for classified normal and abnormal. CONCLUSIONS: This algorithm will be helpful to diagnose supraspinatus tendon tear on ultrasound images.

A comparative reliability and performance study of different stent designs in terms of mechanical properties: foreshortening, recoil, radial force, and flexibility.

This study seeks to improve the mechanical performance of stents by conducting reliability performance testing and finite element method (FEM)-based simulations for coronary stents. Three commercially available stent designs and our own new design were tested to measure the factors affecting performance, specifically foreshortening, recoil, radial force, and flexibility. The stents used in the present experiments were 3 mm in working diameter and 18 mm of working length. The results of the experiments indicate that the foreshortening of stents A, B, C, and our new design, D, was equivalent to 2.25, 0.67, 0.46, and 0.41%, respectively. The recoil of stents A, B, C, and D was 6.00, 4.35, 3.50, and 4.36%, respectively. Parallel plate radial force measurements were A, 3.72 ± 0.28 N; B, 3.81 ± 0.32 N; C, 4.35 ± 0.18 N; and D, 4.02 ± 0.24 N. Radial forces determined by applying uniform pressure in the circumferential direction were A, 28.749 ± 0.81 N; B, 32.231 ± 1.80 N; C, 34.522 ± 3.06 N; and D, 42.183 ± 2.84 N. The maximum force of crimped stent at 2.2-mm deflection was 1.01 ± 0.08 N, 0.82 ± 0.08 N, 0.92 ± 0.12 N, and 0.68 ± 0.07 N for each of stents A, B, C and D. The results of this study enabled us to identify several factors to enhance the performance of stents. In comparing these stents, we found that our design, stent D, which was designed by a collaborative team from seven universities, performed better than the commercial stents across all parameter of foreshortening, recoil, radial force, and flexibility.

Integrated Solution for Physical Activity Monitoring Based on Mobile Phone and PC.

OBJECTIVES: This study is part of the ongoing development of treatment methods for metabolic syndrome (MS) project, which involves monitoring daily physical activity. In this study, we have focused on detecting walking activity from subjects which includes many other physical activities such as standing, sitting, lying, walking, running, and falling. Specially, we implemented an integrated solution for various physical activities monitoring using a mobile phone and PC. METHODS: We put the iPod touch has built in a tri-axial accelerometer on the waist of the subjects, and measured change in acceleration signal according to change in ambulatory movement and physical activities. First, we developed of programs that are aware of step counts, velocity of walking, energy consumptions, and metabolic equivalents based on iPod. Second, we have developed the activity recognition program based on PC. iPod synchronization with PC to transmit measured data using iPhoneBrowser program. Using the implemented system, we analyzed change in acceleration signal according to the change of six activity patterns. RESULTS: We compared results of the step counting algorithm with different positions. The mean accuracy across these tests was 99.6 ± 0.61%, 99.1 ± 0.87% (right waist location, right pants pocket). Moreover, six activities recognition was performed using Fuzzy c means classification algorithm recognized over 98% accuracy. In addition we developed of programs that synchronization of data between PC and iPod for long-term physical activity monitoring. CONCLUSIONS: This study will provide evidence on using mobile phone and PC for monitoring various activities in everyday life. The next step in our system will be addition of a standard value of various physical activities in everyday life such as household duties and a health guideline how to select and plan exercise considering one's physical characteristics and condition.

Gadoxetate disodium-enhanced hepatobiliary phase MRI of hepatocellular carcinoma: correlation with histological characteristics.

OBJECTIVE: The purpose of this study was to assess whether gadoxetate disodium-enhanced hepatobiliary phase MRI could predict the histologic factors of hepatocellular carcinomas (HCCs). MATERIALS AND METHODS: Fifty-three HCCs histopathologically proved by surgery in 51 patients were evaluated retrospectively. All patients underwent gadoxetate disodium-enhanced MRI before surgical resection. The differences in contrast enhancement ratio of the lesions and differences in contrast-to-noise ratio (CNR) among the histologic grades of HCC were compared by using the Kruskal-Wallis test. The Spearman method was used to determine the correlations among contrast enhancement ratio, CNR, cell density ratio, and positivity for anti-hepatocyte antibody, keratin 7, and keratin 19. RESULTS: Of 53 HCCs, 50 showed low signal intensity on hepatobiliary phase images, whereas three HCCs were hyperintense on hepatobiliary phase images compared with surrounding hepatic parenchyma. Although well-differentiated HCCs tended to show higher contrast enhancement, there was no statistical significance between contrast enhancement ratio of the tumors and histologic grade (p = 0.414). No significant difference was observed between CNR and histologic grade (p = 0.965). The contrast enhancement ratios of the tumors were significantly lower in the keratin 19-positive group than in the keratin 19-negative group (p = 0.015). There was no significant correlation among contrast enhancement ratio, anti-hepatocyte antibody positivity, cell density ratio, and keratin 7 positivity (p > 0.05). CONCLUSION: The contrast enhancement ratio and CNR of HCCs were not correlated with histologic grades. The contrast enhancement ratio was significantly lower in keratin 19-positive HCCs.

Electro-Mechanical Safety Testing of Portable ECG Devices for Home Healthcare Usage.

OBJECTIVES: This paper suggests the experimental guidelines to evaluate the electro-mechanical safety of belt type equipment. The electro-mechanical safety was determined by using the International Electrotechnical Commission guidelines, which are widely used as important factors for assessing the electro-mechanical safety of belt type equipment. However, the local guidelines on wearable healthcare sensors are currently not well-established. Therefore, safety guidelines suited for the actual circumstances in Korea are required, and this paper attempts to try a new experimental safety test procedure of the wearable healthcare sensor. METHODS: This belt type device measures the electrocardiogram (ECG) and heart rates by attaching to the chest. Examination lists were selected by analyzing the common standards ofelectro-mechanical safety (IEC 60601-1) and environment tests (IEC 60068-1, IEC 60068-2) of home-healthcare equipment. RESULTS: The essential electrical safety, which was required for the RS300G3 as a medical device, was evaluated, and most of the examination lists were selected by considering the circumstances of the users. The device passed all the selected examinable lists that are applicable to the Korean environment. CONCLUSIONS: This study has limitations to estimate and to conduct electro-mechanical safety experiments because our study focused on the belt type of heart-rates equipment. We are not taking into account the overall electro-mechanical home-healthcare measurements. According to industrial and technological development, there are infinite possibilities for the advancement of home-healthcare equipment, so more examination lists for safety are being added in addition to what we have done.

Non-linear Analysis of Single Electroencephalography (EEG) for Sleep-Related Healthcare Applications.

OBJECTIVES: Soft-computing techniques are commonly used to detect medical phenomena and to help with clinical diagnoses and treatment. The purpose of this paper is to analyze the single electroencephalography (EEG) signal with the chaotic methods in order to identify the sleep stages. METHODS: Data acquisition (polysomnography) was performed on four healthy young adults (all males with a mean age of 27.5 years). The evaluated algorithm was designed with a correlation dimension and Lyapunov's exponent using a single EEG signal that detects differences in chaotic characteristics. RESULTS: The change of the correlation dimension and the largest Lyapunov exponent over the whole night sleep EEG was performed. The results show that the correlation dimension and largest Lyapunov exponent decreased from light sleep to deep sleep and they increased during the rapid eye movement stage. CONCLUSIONS: These results suggest that chaotic analysis may be a useful adjunct to linear (spectral) analysis for identifying sleep stages. The single EEG based nonlinear analysis is suitable for u-healthcare applications for monitoring sleep.

Ubiquitous-severance hospital project: implementation and results.

OBJECTIVES: The purpose of this study was to review an implementation of u-Severance information system with focus on electronic hospital records (EHR) and to suggest future improvements. METHODS: Clinical Data Repository (CDR) of u-Severance involved implementing electronic medical records (EMR) as the basis of EHR and the management of individual health records. EHR were implemented with service enhancements extending to the clinical decision support system (CDSS) and expanding the knowledge base for research with a repository for clinical data and medical care information. RESULTS: The EMR system of Yonsei University Health Systems (YUHS) consists of HP integrity superdome servers using MS SQL as a database management system and MS Windows as its operating system. CONCLUSIONS: YUHS is a high-performing medical institution with regards to efficient management and customer satisfaction; however, after 5 years of implementation of u-Severance system, several limitations with regards to expandability and security have been identified.