The Development of an Artificial Intelligence Video Analysis-Based Web Application to Diagnose Oropharyngeal Dysphagia: A Pilot Study.

The gold standard test for diagnosing dysphagia is the videofluoroscopic swallowing study (VFSS). However, the accuracy of this test varies depending on the specialist's skill level. We proposed a VFSS-based artificial intelligence (AI) web application to diagnose dysphagia. Video from the VFSS consists of multiframe data that contain approximately 300 images. To label the data, the server separated them into frames during the upload and stored them as a video for analysis. Then, the separated data were loaded into a labeling tool to perform the labeling. The labeled file was downloaded, and an AI model was developed by training with You Only Look Once (YOLOv7). Using a utility called SplitFolders, the entire dataset was divided according to a ratio of training (70%), test (10%), and validation (20%). When a VFSS video file was uploaded to an application equipped with the developed AI model, it was automatically classified and labeled as oral, pharyngeal, or esophageal. The dysphagia of a person was categorized as either penetration or aspiration, and the final analyzed result was displayed to the viewer. The following labeling datasets were created for the AI learning: oral (n = 2355), pharyngeal (n = 2338), esophageal (n = 1480), penetration (n = 1856), and aspiration (n = 1320); the learning results of the YOLO model, which analyzed dysphagia using the dataset, were predicted with accuracies of 0.90, 0.82, 0.79, 0.92, and 0.96, respectively. This is expected to help clinicians more efficiently suggest the proper dietary options for patients with oropharyngeal dysphagia.

Association between Body Composition Contents and Hepatic Fibrosis in Sarcopenic Obesity.

It is well established that sarcopenic obesity (SO) is linked to many diseases such as metabolic and non-alcoholic fatty liver diseases, but there is little known about the relationship between SO and hepatic fibrosis progression in chronic liver disease. This study compared body composition contents in patients with non-obesity (NOb) and SO using abdominal magnetic resonance imaging and investigated the relationship between hepatic fibrosis and SO factors. This retrospective study enrolled 60 patients (28 NOb; 32 SO) from June 2014 to December 2020. Patients underwent histopathologic investigation where they classified fibrosis stages based on the Meta-analysis of Histological Data in Viral Hepatitis fibrosis scoring system. Muscle and fat areas at the third lumber vertebra level were assessed. The variation in the areas of muscle (MA), subcutaneous adipose tissue (SAT), and visceral adipose tissue (VAT) among fibrosis stages, and associations between hepatic fibrosis and SO factors, were analyzed. There were significant differences in SAT and VAT (p < 0.001), whereas there was no difference in MA (p = 0.064). There were significant differences in MA/SAT (p = 0.009), MA/VAT (p < 0.001), and MA/(SAT+VAT) (p < 0.001). In all the patients, hepatic fibrosis positively correlated with serum aspartate aminotransferase level (AST, R = 0.324; p = 0.025). Especially in SO patients, hepatic fibrosis closely correlated with body mass index (BMI, R = 0.443; p = 0.011), AST (R = 0.415; p = 0.044), VAT (R = 0.653; p < 0.001), MA/VAT (R = -0.605; p < 0.001), and MA/(SAT+VAT) (R = -0.416; p = 0.018). However, there was no association in NOb patients. This study demonstrated that SO patients had larger SAT and VAT than NOb patients. Hepatic fibrosis in SO positively correlated with body visceral fat composition in combination with BMI and AST level. These findings will be useful for understanding the relationship between the hepatic manifestation of fibrosis and body fat composition in sarcopenia and SO.

Association between impaired glucose metabolism and long-term prognosis at the time of diagnosis of depression: Impaired glucose metabolism as a promising biomarker proposed through a machine-learning approach.

BACKGROUND: Predicting the course of depression is necessary for personalized treatment. Impaired glucose metabolism (IGM) was introduced as a promising depression biomarker, but no consensus was made. This study aimed to predict IGM at the time of depression diagnosis and examine the relationship between long-term prognosis and predicted results. METHODS: Clinical data were extracted from four electronic health records in South Korea. The study population included patients with depression, and the outcome was IGM within 1 year. One database was used to develop the model using three algorithms. External validation was performed using the best algorithm across the three databases. The area under the curve (AUC) was calculated to determine the model's performance. Kaplan-Meier and Cox survival analyses of the risk of hospitalization for depression as the long-term outcome were performed. A meta-analysis of the long-term outcome was performed across the four databases. RESULTS: A prediction model was developed using the data of 3,668 people, with an AUC of 0.781 with least absolute shrinkage and selection operator (LASSO) logistic regression. In the external validation, the AUCs were 0.643, 0.610, and 0.515. Through the predicted results, survival analysis and meta-analysis were performed; the hazard ratios of risk of hospitalization for depression in patients predicted to have IGM was 1.20 (95% confidence interval [CI] 1.02-1.41, p = 0.027) at a 3-year follow-up. CONCLUSIONS: We developed prediction models for IGM occurrence within a year. The predicted results were related to the long-term prognosis of depression, presenting as a promising IGM biomarker related to the prognosis of depression.

Gray matter differences associated with menopausal hormone therapy in menopausal women: a DARTEL-based VBM study.

Menopausal hormone therapy (MHT) in women can reduce troublesome menopause symptoms and prevent cognitive decline. This cross-sectional study investigated the MHT-related effect on brain morphology and its association with sex hormones in menopausal women by using an optimized diffeomorphic anatomical registration through exponentiated Lie algebra (DARTEL)-based voxel-based morphometry (VBM) method. Twenty-one menopausal women without MHT (noMHT) and 20 menopausal women with MHT were included in this study. Magnetic resonance imaging data were processed using SPM 12 with DARTEL-based VBM whole brain analysis approach. A 2-sample t-test and analysis of covariance (ANCOVA) adjusting for age and total intracranial volume were used to compare GM volume between noMHT and MHT women. The association between MHT (treatment period, hormones levels) and brain volume variations were analyzed by Spearman correlation. MHT women showed significantly larger volumes of the superior/middle/inferior frontal gyri, hypothalamus, inferior temporal gyrus, parahippocampal gyrus, hippocampus, cerebellar cortex, postcentral gyrus, precuneus, angular gyrus, supplementary motor area, superior occipital gyrus, and precentral gyrus compared to the noMHT women. The volumes of the angular gyrus and hypothalamus in MHT women positively correlated with treatment period. On the other hand, the hypothalamic volume negatively correlated with FSH and LH levels, and the volumes of the inferior frontal gyrus, and angular gyrus negatively correlated with progesterone levels, respectively. MHT-treated women showed larger GM volume than noMHT women. The anatomical structures that showed greater volume in association with MHT included the deep brain areas, frontal, temporal, parietal, and occipital gyri.

Artificial intelligence for predicting five-year survival in stage IV metastatic breast cancer patients: A focus on sarcopenia and other host factors.

We developed an artificial intelligence (AI) model that can predict five-year survival in patients with stage IV metastatic breast cancer, mainly based on host factors and sarcopenia. From a prospectively built breast cancer registry, a total of 210 metastatic breast cancer patients were selected in a consecutive manner using inclusion/exclusion criteria. The patients' data were divided into two categories: a group that survived for more than 5 years and a group that did not survive for 5 years. For the AI model input, 11 features were considered, including age, body mass index, skeletal muscle area (SMA), height-relative SMA (H-SMI), height square-relative SMA (H2-SMA), weight-relative SMA (W-SMA), muscle mass, anticancer chemotherapy, radiation therapy, and comorbid diseases such as hypertension and mellitus. For the feature importance analysis, we compared classifiers using six different machine learning algorithms and found that extreme gradient boosting (XGBoost) provided the best accuracy. Subsequently, we performed the feature importance analysis based on XGBoost and proposed a 4-layer deep neural network, which considered the top 10 ranked features. Our proposed 4-layer deep neural network provided high sensitivity (75.00%), specificity (78.94%), accuracy (78.57%), balanced accuracy (76.97%), and an area under receiver operating characteristics of 0.90. We generated a web application for anyone to easily access and use this AI model to predict five-year survival. We expect this web application to be helpful for patients to understand the importance of host factors and sarcopenia and achieve survival gain.

Regional Analysis of Liver Surface Nodularity in a Single Axial MR Image for Staging Liver Fibrosis.

BACKGROUND: The assessment of liver surface nodularity (LSN) for staging hepatic fibrosis is restricted in clinical practice because it requires customized software and time-consuming procedures. A simplified method to estimate LSN score may be useful in the clinic. PURPOSE: To evaluate the regional analysis of LSN and processing time in a single axial liver MR image for staging liver fibrosis. STUDY TYPE: Retrospective. POPULATION: A total of 210 subjects, a multicenter study. FIELD STRENGTH/SEQUENCE: A 3 T/noncontrast gradient echo T1WI. ASSESSMENT: Subjects were divided into five fibrosis groups (F0  = 29; F1  = 20; F2  = 32; F3  = 50; F4  = 79) based on the METAVIR fibrosis scoring system. The mean LSN (on three slices) and regional LSN (on one slice) measurements, and the processing times, are compared. The regional LSN scores in five regions-of-interests (ROI1-5 ) were analyzed in a single axial MRI at the level of the hilum by two independent observers. STATISTICAL TESTS: Regional variations in LSN scores were compared using ANOVA with Tukey test. Agreement between the mean and regional LSN measurements was evaluated using Pearson correlation coefficients (r) and Bland-Altman plots. The diagnostic performance of mean and regional LSN scores according to fibrosis stage was evaluated with the AUROC. A P value < 0.05 was considered statistically significant. RESULTS: Total processing time for a regional LSN measurement (3.6 min) was 75.5% less than that for mean LSN measurement (14.7 min). Mean LSN scores and all five regional LSN scores showed significant differences between fibrosis groups. Among regional LSN scores, ROI5 showed the highest AUROC (0.871 at cut-off 1.12) for discriminating F0-2 vs. F3-4 and the best correlation with mean LSN score (r = 0.800, -0.07 limit of agreement). CONCLUSION: Quantitative regional LSN measurement in a single axial MR image reduces processing time. Regional ROI5 LSN score might be useful for clinical decision-making and for distinguishing the difference between early fibrosis (F0-2 ) and advanced fibrosis (F3-4 ) in the liver. EVIDENCE LEVEL: 3 TECHNICAL EFFICACY: Stage 2.

Development and validation of a management system and dataset quality assessment tool for the Radiology Common Data Model (R_CDM): A case study in liver disease.

BACKGROUND: The Observational Medical Outcomes Partnership-Common Data Model (OMOP-CDM), a distributed research network, has low clinical data coverage. Radiological data are valuable, but imaging metadata are often incomplete, and a standardized recording format in the OMOP-CDM is lacking. We developed a web-based management system and data quality assessment (RQA) tool for a radiology_CDM (R_CDM) and evaluated the feasibility of clinically applying this dataset. METHODS: We designed an R_CDM with Radiology_Occurrence and Radiology_Image tables. This was seamlessly linked to the OMOP-CDM clinical data. We adopted the standardized terminology using the RadLex playbook and mapped 5,753 radiology protocol terms to the OMOP vocabulary. An extract, transform, and load (ETL) process was developed to extract detailed information that was difficult to extract from metadata and to compensate for missing values. Image-based quantification was performed to measure liver surface nodularity (LSN), using customized Wonkwang abdomen and liver total solution (WALTS) software. RESULTS: On a PACS, 368,333,676 DICOM files (1,001,797 cases) were converted to R_CDM chronic liver disease (CLD) data (316,596 MR images, 228 cases; 926,753 CT images, 782 cases) and uploaded to a web-based management system. Acquisition date and resolution were extracted accurately, but other information, such as "contrast administration status" and "photography direction", could not be extracted from the metadata. Using WALTS, 9,609 pre-contrast axial-plane abdominal MR images (197 CLD cases) were assigned LSN scores by METAVIR fibrosis grades, which differed significantly by ANOVA (p < 0.001). The mean RQA score (83.5) indicated good quality. CONCLUSION: This study developed a web-based system for management of the R_CDM dataset, RQA tool, and constructed a CLD R_CDM dataset, with good quality for clinical application. Our management system and R_CDM CLD dataset would be useful for multicentric and image-based quantification researches.

Analysis of treatment pattern of anti-dementia medications in newly diagnosed Alzheimer's dementia using OMOP CDM.

Anti-dementia medications are widely prescribed to patients with Alzheimer's dementia (AD) in South Korea. This study investigated the pattern of medical management in newly diagnosed patients with AD using a standardized data format-the Observational Medical Outcome Partnership Common Data Model from five hospitals. We examined the anti-dementia treatment patterns from datasets that comprise > 5 million patients during 2009-2019. The medication utility information was analyzed with respect to treatment trends and persistence across 11 years. Among the 8653 patients with newly diagnosed AD, donepezil was the most commonly prescribed anti-dementia medication (4218; 48.75%), followed by memantine (1565; 18.09%), rivastigmine (1777; 8.98%), and galantamine (494; 5.71%). The rising prescription trend during observation period was found only with donepezil. The treatment pathways for the three cholinesterase inhibitors combined with N-methyl-D-aspartate receptor antagonist were different according to the drugs (19.6%; donepezil; 28.1%; rivastigmine, and 17.2%; galantamine). A 12-month persistence analysis showed values of approximately 50% for donepezil and memantine and approximately 40% for rivastigmine and galantamine. There were differences in the prescribing pattern and persistence among anti-dementia medications from database using the Observational Medical Outcome Partnership Common Data Model on the Federated E-health Big Data for Evidence Renovation Network platform in Korea.

Optimal Deep-Learning-Enabled Intelligent Decision Support System for SARS-CoV-2 Classification.

Intelligent decision support systems (IDSS) for complex healthcare applications aim to examine a large quantity of complex healthcare data to assist doctors, researchers, pathologists, and other healthcare professionals. A decision support system (DSS) is an intelligent system that provides improved assistance in various stages of health-related disease diagnosis. At the same time, the SARS-CoV-2 infection that causes COVID-19 disease has spread globally from the beginning of 2020. Several research works reported that the imaging pattern based on computed tomography (CT) can be utilized to detect SARS-CoV-2. Earlier identification and detection of the diseases is essential to offer adequate treatment and avoid the severity of the disease. With this motivation, this study develops an efficient deep-learning-based fusion model with swarm intelligence (EDLFM-SI) for SARS-CoV-2 identification. The proposed EDLFM-SI technique aims to detect and classify the SARS-CoV-2 infection or not. Also, the EDLFM-SI technique comprises various processes, namely, data augmentation, preprocessing, feature extraction, and classification. Moreover, a fusion of capsule network (CapsNet) and MobileNet based feature extractors are employed. Besides, a water strider algorithm (WSA) is applied to fine-tune the hyperparameters involved in the DL models. Finally, a cascaded neural network (CNN) classifier is applied for detecting the existence of SARS-CoV-2. In order to showcase the improved performance of the EDLFM-SI technique, a wide range of simulations take place on the COVID-19 CT data set and the SARS-CoV-2 CT scan data set. The simulation outcomes highlighted the supremacy of the EDLFM-SI technique over the recent approaches.

Artificial-intelligence-driven discovery of prognostic biomarker for sarcopenia.

BACKGROUND: Sarcopenia is defined as muscle wasting, characterized by a progressive loss of muscle mass and function due to ageing. Diagnosis of sarcopenia typically involves both muscle imaging and the physical performance of people exhibiting signs of muscle weakness. Despite its worldwide prevalence, a molecular method for accurately diagnosing sarcopenia has not been established. METHODS: We develop an artificial intelligence (AI) diagnosis model of sarcopenia using a published transcriptome dataset comprising patients from multiple ethnicities. For the AI model for sarcopenia diagnosis, we use a transcriptome database comprising 17 339 genes from 118 subjects. Among the 17 339 genes, we select 27 features as the model inputs. For feature selection, we use a random forest, extreme gradient boosting and adaptive boosting. Using the top 27 features, we propose a four-layer deep neural network, named DSnet-v1, for sarcopenia diagnosis. RESULTS: Among isolated testing datasets, DSnet-v1 provides high sensitivity (100%), specificity (94.12%), accuracy (95.83%), balanced accuracy (97.06%) and area under receiver operating characteristics (0.99). To extend the number of patient data, we develop a web application (http://sarcopeniaAI.ml/), where the model can be accessed unrestrictedly to diagnose sarcopenia if the transcriptome is available. A focused analysis of the top 27 genes for their differential or co-expression with other genes implied the potential existence of race-specific factors for sarcopenia, suggesting the possibility of identifying causal factors of sarcopenia when a more extended dataset is provided. CONCLUSIONS: Our new AI model, DSnet-v1, accurately diagnoses sarcopenia and is currently available publicly to assist healthcare providers in diagnosing and treating sarcopenia.

Assessment of Liver Fibrosis Stage Using Integrative Analysis of Hepatic Heterogeneity and Nodularity in Routine MRI with FIB-4 Index as Reference Standard.

Image-based quantitative methods for liver heterogeneity (LHet) and nodularity (LNod) provide helpful information for evaluating liver fibrosis; however, their combinations are not fully understood in liver diseases. We developed an integrated software for assessing LHet and LNod and compared LHet and LNod according to fibrosis stages in chronic liver disease (CLD). Overall, 111 CLD patients and 16 subjects with suspected liver disease who underwent liver biopsy were enrolled. The procedures for quantifying LHet and LNod were bias correction, contour detection, liver segmentation, and LHet and LNod measurements. LHet and LNod scores among fibrosis stages (F0-F3) were compared using ANOVA with Tukey's test. Diagnostic accuracy was determined by calculating the area under the receiver operating characteristics (AUROC) curve. The mean LHet scores of F0, F1, F2, and F3 were 3.49 ± 0.34, 5.52 ± 0.88, 6.80 ± 0.97, and 7.56 ± 1.79, respectively (p < 0.001). The mean LNod scores of F0, F1, F2, and F3 were 0.84 ± 0.06, 0.91 ± 0.04, 1.09 ± 0.08, and 1.15 ± 0.14, respectively (p < 0.001). The combined LHet × LNod scores of F0, F1, F2, and F3 were 2.96 ± 0.46, 5.01 ± 0.91, 7.30 ± 0.89, and 8.48 ± 1.34, respectively (p < 0.001). The AUROCs of LHet, LNod, and LHet × LNod for differentiating F1 vs. F2 and F2 vs. F3 were 0.845, 0.958, and 0.954; and 0.619, 0.689, and 0.761, respectively. The combination of LHet and LNod scores derived from routine MR images allows better differential diagnosis of fibrosis subgroups in CLD.

Development of quantification software for evaluating body composition contents and its clinical application in sarcopenic obesity.

In sarcopenic obesity, the importance of evaluating muscle and fat mass is unquestionable. There exist diverse quantification methods for assessing muscle and fat mass by imaging techniques; thus these methods must be standardized for clinical practice. This study developed a quantification software for the body composition imaging using abdominal magnetic resonance (MR) images and compared the difference between sarcopenic obesity and healthy controls for clinical application. Thirty patients with sarcopenic obesity and 30 healthy controls participated. The quantification software was developed based on an ImageJ multiplatform and the processing steps are as follows: execution, setting, confirmation, and extraction. The variation in the muscle area (MA), subcutaneous fat area (SA), and visceral fat area (VA) was analyzed with an independent two sample T-test. There were significant differences in SA (p < 0.001) and VA (p = 0.011), whereas there was no difference in MA (p = 0.421). Regarding the ratios, there were significant differences in MA/SA (p < 0.001), MA/VA (p = 0.002), and MA/(SA + VA) (p < 0.001). Overall, intraclass correlation coefficients were higher than 0.9, indicating excellent reliability. This study developed customized sarcopenia-software for assessing body composition using abdominal MR images. The clinical findings demonstrate that the quantitative body composition areas and ratios can assist in the differential diagnosis of sarcopenic obesity or sarcopenia.

Remote Pulmonary Function Test Monitoring in Cloud Platform via Smartphone Built-in Microphone.

With an aging population that continues to grow, health care technology plays an increasingly active role, especially for chronic disease management. In the health care market, cloud platform technology is becoming popular, as both patients and physicians demand cost efficiency, easy access to information, and security. Especially for asthma and chronic obstructive pulmonary disease (COPD) patients, it is recommended that pulmonary function test (PFT) be performed on a daily basis. However, it is difficult for patients to frequently visit a hospital to perform the PFT. In this study, we present an application and cloud platform for remote PFT monitoring that can be directly measured by smartphone microphone with no external devices. In addition, we adopted the IBM Watson Internet-of-Things (IoT) platform for PFT monitoring, using a smartphone's built-in microphone with a high-resolution time-frequency representation. We successfully demonstrated real-time PFT monitoring using the cloud platform. The PFT parameters of FEV1/FVC (%) could be remotely monitored when a subject performed the PFT test. As a pilot study, we tested 13 healthy subjects, and found that the absolute error mean was 4.12 and the standard deviation was 3.45 on all 13 subjects. With the developed applications on the cloud platform, patients can freely measure the PFT parameters without restriction on time and space, and a physician can monitor the patients' status in real time. We hope that the PFT monitoring platform will work as a means for early detection and treatment of patients with pulmonary diseases, especially those having asthma and COPD.

Accuracy of proton magnetic resonance for diagnosing non-alcoholic steatohepatitis: a meta-analysis.

Liver biopsy is the reference standard test to differentiate between non-alcoholic steatohepatitis (NASH) and simple steatosis (SS) in non-alcoholic fatty liver disease (NAFLD), but noninvasive diagnostics are warranted. The diagnostic accuracy in NASH using MR imaging modality have not yet been clearly identified. This study was assessed the accuracy of magnetic resonance imaging (MRI) method for diagnosing NASH. Data were extracted from research articles obtained after a literature search from multiple electronic databases. Random-effects meta-analyses were performed to obtain overall effect size of the area under the receiver operating characteristic(ROC) curve, sensitivity, specificity, likelihood ratios(LR), diagnostic odds ratio(DOR) of MRI method in detecting histopathologically-proven SS(or non-NASH) and NASH. Seven studies were analyzed 485 patients, which included 207 SS and 278 NASH. The pooled sensitivity was 87.4% (95% CI, 76.4-95.3) and specificity was 74.3% (95% CI, 62.4-84.6). Pooled positive LR was 2.59 (95% CI, 1.96-3.42) and negative LR was 0.17 (95% CI, 0.07-0.38). DOR was 21.57 (95% CI, 7.27-63.99). The area under the curve of summary ROC was 0.89. Our meta-analysis shows that the MRI-based diagnostic methods are valuable additions in detecting NASH.

Development of liver surface nodularity quantification program and its clinical application in nonalcoholic fatty liver disease.

The liver morphological changes in relation to fibrosis stage in nonalcoholic fatty liver disease (NAFLD) have not yet been clearly understood. This study was to develop a liver surface nodularity (LSN) quantification program and to compare the fibrosis grades in simple steatosis (SS) and nonalcoholic steatohepatitis (NASH). Thirty subjects (7 normal controls [NC], 12 SS and 11 NASH) were studied. LSN quantification procedure was bias correction, boundary detection, segmentation and LSN measurement. LSN scores among three groups and fibrosis grades compared using Kruskal-Wallis H test. Diagnostic accuracy was determined by calculating the area under the receiver operating characteristics (ROC) curve. Mean LSN scores were NC 1.30 ± 0.09, SS 1.54 ± 0.21 and NASH 1.59 ± 0.23 (p = 0.008). Mean LSN scores according to fibrosis grade (F) were F0 1.30 ± 0.09, F1 1.45 ± 0.17 and F2&F3 1.67 ± 0.20 (p = 0.001). The mean LSN score in F2&F3 is significantly higher than that in F1 (p = 0.019). The AUROC curve to distinguish F1 and F2&F3 was 0.788 (95% CI 0.595-0.981, p = 0.019) at a cut-off LSN score greater than 1.48, and its diagnostic accuracy had 0.833 sensitivity and 0.727 specificity. This study developed LSN program and its clinical application demonstrated that the quantitative LSN scores can help to differentially diagnose fibrosis stage in NAFLD.

Development of mobile intraoperative computed tomography imaging system and assessment of its performance in a brain and body phantom study.

BACKGROUND: Intraoperative computed tomography (iCT) system has been developed focusing on combining the advanced imaging techniques for the best imaging modality. However, the use of iCT system in the operating rooms is limited due to the lack of flexible mobility. OBJECTIVE: This study aims to develop a mobile iCT imaging system and assess its imaging performance in a phantom study. METHODS: The mobile iCT system with mecanum omni-directional wheels has three major components namely, a rotating gantry, a slip-ring and a stationary gantry. Performance of mecanum iCT system was evaluated using the indices of signal-to-noise (SNR), contrast-to noise (CNR), and spatial resolution (MTF). Anatomical landmarks on phantom images were assessed using a 5-point scale (5 = definitely seen; 4 = probably seen; 3 = equivocal; 2 = probably not seen; and 1 = definitely not seen). RESULTS: The mecanum iCT system can be conveniently used for a whole-body scan under intraoperative conditions even in narrow operating rooms due to a smaller turning radius. The image quality of the mecanum iCT system was found to be acceptable for clinical applications (with SNR = 162.72, CNR = 134.29 and MTF = 694 µm). The diagnostic scores on the phantom images were 'definitely seen' value. CONCLUSIONS: The proposed mecanum iCT system achieved the improved flexible mobility and has potential to better serve as a useful imaging tool in the clinical intraoperative setting.

Noninvasive Differential Diagnosis of Liver Iron Contents in Nonalcoholic Steatohepatitis and Simple Steatosis Using Multiecho Dixon Magnetic Resonance Imaging.

RATIONALE AND OBJECTIVES: The roles of iron stores in nonalcoholic fatty liver disease have not yet been clearly identified, and it is lack of uniform criteria and a standardized study design for assessing the liver iron content (LIC) in nonalcoholic steatohepatitis (NASH). This study was to compare LICs in biopsy-proven simple steatosis (SS) and NASH based on T2⁎-relaxometry. MATERIAL AND METHODS: A total of 32 subjects divided to three groups, consisting of 10 healthy controls, 12 SS and 10 NASH. All MRI examinations were performed on a 3 T MRI with a 32-channel body coil. To measure T2⁎-value, we used a gradient echo sequence with six multiechoes within a single breath-hold. Hepatic iron contents among three groups were compared using Kruskal-Wallis H test and Mann-Whitney's posthoc tests. Diagnostic accuracy was determined by calculating the area under the receiver operating characteristics curve. To identify the reliability of iron measurements in the different region of interests, coefficient of variance (CV) was calculated overall CV values for the variability of measurements. Interobserver agreement and reliability were estimated by calculating the intraclass correlation coefficient. RESULTS: The variations of all LIC measurements are not exceeded 20%, as a mean CV value 18.3%. intraclass correlation coefficients were higher than 0.9. Mean T2⁎-values at localized region of interests were healthy controls 45.42 ± 7.19 ms, SS 20.96 ± 4.28 ms, and NASH 15.49 ± 2.87 ms. The mean T2⁎-value in NASH is significantly shorter than that in SS (p = 0.008). The area under the receiver operating characteristics curve to distinguish NASH from SS was 0.908 (95% confidence interval 0.775-1.000, p = 0.001) at a cut-off of iron contents greater than 17.95 ms, and its diagnostic accuracy had 0.833 sensitivity and 0.800 specificity. CONCLUSION: This study demonstrates that the T2⁎ calculation can help to differentially diagnose NASH.

Dedicated cardiac rehabilitation wearable sensor and its clinical potential.

We describe a wearable sensor developed for cardiac rehabilitation (CR) exercise. To effectively guide CR exercise, the dedicated CR wearable sensor (DCRW) automatically recommends the exercise intensity to the patient by comparing heart rate (HR) measured in real time with a predefined target heart rate zone (THZ) during exercise. The CR exercise includes three periods: pre-exercise, exercise with intensity guidance, and post-exercise. In the pre-exercise period, information such as THZ, exercise type, exercise stage order, and duration of each stage are set up through a smartphone application we developed for iPhones and Android devices. The set-up information is transmitted to the DCRW via Bluetooth communication. In the period of exercise with intensity guidance, the DCRW continuously estimates HR using a reflected pulse signal in the wrist. To achieve accurate HR measurements, we used multichannel photo sensors and increased the chances of acquiring a clean signal. Subsequently, we used singular value decomposition (SVD) for de-noising. For the median and variance of RMSEs in the measured HRs, our proposed method with DCRW provided lower values than those from a single channel-based method and template-based multiple-channel method for the entire exercise stage. In the post-exercise period, the DCRW transmits all the measured HR data to the smartphone application via Bluetooth communication, and the patient can monitor his/her own exercise history.

Development of Portable Digital Radiography System with a Device for Monitoring X-ray Source-Detector Angle and Its Application in Chest Imaging.

This study developed a device measuring the X-ray source-detector angle (SDA) and evaluated the imaging performance for diagnosing chest images. The SDA device consisted of Arduino, an accelerometer and gyro sensor, and a Bluetooth module. The SDA values were compared with the values of a digital angle meter. The performance of the portable digital radiography (PDR) was evaluated using the signal-to-noise (SNR), contrast-to-noise ratio (CNR), spatial resolution, distortion and entrance surface dose (ESD). According to different angle degrees, five anatomical landmarks were assessed using a five-point scale. The mean SNR and CNR were 182.47 and 141.43. The spatial resolution and ESD were 3.17 lp/mm (157 µm) and 0.266 mGy. The angle values of the SDA device were not significantly difference as compared to those of the digital angle meter. In chest imaging, the SNR and CNR values were not significantly different according to the different angle degrees. The visibility scores of the border of the heart, the fifth rib and the scapula showed significant differences according to different angles (p < 0.05), whereas the scores of the clavicle and first rib were not significant. It is noticeable that the increase in the SDA degree was consistent with the increases of the distortion and visibility score. The proposed PDR with a SDA device would be useful for application in the clinical radiography setting according to the standard radiography guidelines.

Reduced radiation dose and improved image quality using a mini mobile digital imaging system in a neonatal intensive care unit.

This study was aimed to assess the radiation dose and image quality of a mini-mobile digital imaging (mini-DI) system for neonatal chest radiography and compared to conventional digital radiography (DR). A total of 64 neonates were examined and anatomical landmarks were assessed. The entrance surface dose of mini DI and conventional DR was 26.64±0.15 µGy and 49.11±1.46 µGy, respectively (p<0.001). The mean SNR values for mini-DI and DR were 233.2±5.1 and 31.6±1.2, and 10% MTF values were 131 and 161µm. A newly developed mini-DI is capable of preserving the diagnostic information with dose reduction in neonates under intensive care.