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.

Comparison of Finasteride and Dutasteride on Risk of Prostate Cancer in Patients with Benign Prostatic Hyperplasia: A Pooled Analysis of 15 Real-world Databases.

PURPOSE: Finasteride and dutasteride are used to treat benign prostatic hyperplasia (BPH) and reduce the risk of developing prostate cancer. Finasteride blocks only the type 2 form of 5-alpha-reductase, whereas dutasteride blocks both type 1 and 2 forms of the enzyme. Previous studies suggest the possibility that dutasteride may be superior to finasteride in preventing prostate cancer. We directly compared the effects of finasteride and dutasteride on the risk of prostate cancer in patients with BPH using a pooled analysis of 15 real-world databases. MATERIALS AND METHODS: We conducted a multicenter, cohort study of new-users of finasteride and dutasteride. We include patients who were prescribed 5 mg finasteride or dutasteride for the first time to treat BPH and had at least 180 days of prescription. We excluded patients with a history of prostate cancer or a prostate-specific antigen level ≥ 4 ng/mL before the study drug prescription. Cox regression analysis was performed to examine the hazard ratio (HR) for prostate cancer after propensity score (PS) matching. RESULTS: A total of 8,284 patients of new-users of finasteride and 8,670 patients of new-users of dutasteride were included across the 15 databases. In the overall population, compared to dutasteride, finasteride was associated with a lower risk of prostate cancer in both on-treatment and intent-to-treat time-at-risk periods. After 1:1 PS matching, 4,897 patients using finasteride and 4,897 patients using dutasteride were enrolled in the present study. No significant differences were observed for risk of prostate cancer between finasteride and dutasteride both on-treatment (HR=0.66, 95% confidence interval [CI]: 0.44-1.00; p=0.051) and intent-to-treat time-at-risk periods (HR=0.87, 95% CI: 0.67-1.14; p=0.310). CONCLUSIONS: Using real-world databases, the present study demonstrated that dutasteride was not associated with a lower risk of prostate cancer than finasteride in patients with BPH.

Perioperative adverse cardiac events and mortality after non-cardiac surgery: a multicenter study.

BACKGROUND: Perioperative adverse cardiac events (PACE), a composite of myocardial infarction, coronary revascularization, congestive heart failure, arrhythmic attack, acute pulmonary embolism, cardiac arrest, and stroke during 30-day postoperative period, is associated with long-term mortality, but with limited clinical evidence. We compared long-term mortality with PACE using data from nationwide multicenter electronic health records. METHODS: Data from 7 hospitals, converted to Observational Medical Outcomes Partnership Common Data Model, were used. We extracted records of 277,787 adult patients over 18 years old undergoing non-cardiac surgery for the first time at the hospital and had medical records for more than 180 days before surgery. We performed propensity score matching and then an aggregated meta­analysis. RESULTS: After 1:4 propensity score matching, 7,970 patients with PACE and 28,807 patients without PACE were matched. The meta­analysis showed that PACE was associated with higher one-year mortality risk (hazard ratio [HR]: 1.33, 95% CI [1.10, 1.60], P = 0.005) and higher three-year mortality (HR: 1.18, 95% CI [1.01, 1.38], P = 0.038). In subgroup analysis, the risk of one-year mortality by PACE became greater with higher-risk surgical procedures (HR: 1.20, 95% CI [1.04, 1.39], P = 0.020 for low-risk surgery; HR: 1.69, 95% CI [1.45, 1.96], P < 0.001 for intermediate-risk; and HR: 2.38, 95% CI [1.47, 3.86], P = 0.034 for high-risk). CONCLUSIONS: A nationwide multicenter study showed that PACE was significantly associated with increased one-year mortality. This association was stronger in high-risk surgery, older, male, and chronic kidney disease subgroups. Further studies to improve mortality associated with PACE are needed.

Clinical implication of thoracic skeletal muscle volume as a predictor of ventilation-weaning failure in brain-injured patients: A retrospective observational study.

Sarcopenia, a generalized loss of skeletal muscle mass that is primarily evident in the respiratory musculature, is associated with adverse outcomes in critically ill patients. However, the relationship between sarcopenia and ventilation-weaning outcomes has not yet been fully studied in patients with brain injuries. In this study, we examined the effect of reduced respiratory muscle mass on ventilation weaning in patients with brain injury. This observational study retrospectively reviewed the medical records of 73 patients with brain injury between January 2017 and December 2019. Thoracic skeletal muscle volumes were measured from thoracic CT images using the institute's three-dimensional modeling software program of our institute. The thoracic skeletal muscle volumes index (TSMVI) was normalized by dividing muscle volume by the square of patient height. Sarcopenia was defined as a TSMVI of less than the 50th sex-specific percentile. Among 73 patients with brain injury, 12 (16.5%) failed to wean from mechanical ventilation. The patients in the weaning-failure group had significantly higher sequential organ failure assessment scores [7.8 ±â€…2.7 vs 6.1 ±â€…2.2, P = .022] and lower thoracic skeletal muscle volume indexes [652.5 ±â€…252.4 vs 1000.4 ±â€…347.3, P = .002] compared with those in the weaning-success group. In multivariate analysis, sarcopenia was significantly associated with an increased risk of weaning failure (odds ratio 12.72, 95% confidence interval 2.87-70.48, P = .001). Our study showed a significant association between the TSMVI and ventilation weaning outcomes in patients with brain injury.

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.

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.

Development and Validation of the Radiology Common Data Model (R-CDM) for the International Standardization of Medical Imaging Data.

PURPOSE: Digital Imaging and Communications in Medicine (DICOM), a standard file format for medical imaging data, contains metadata describing each file. However, metadata are often incomplete, and there is no standardized format for recording metadata, leading to inefficiency during the metadata-based data retrieval process. Here, we propose a novel standardization method for DICOM metadata termed the Radiology Common Data Model (R-CDM). MATERIALS AND METHODS: R-CDM was designed to be compatible with Health Level Seven International (HL7)/Fast Healthcare Interoperability Resources (FHIR) and linked with the Observational Medical Outcomes Partnership (OMOP)-CDM to achieve a seamless link between clinical data and medical imaging data. The terminology system was standardized using the RadLex playbook, a comprehensive lexicon of radiology. As a proof of concept, the R-CDM conversion process was conducted with 41.7 TB of data from the Ajou University Hospital. The R-CDM database visualizer was developed to visualize the main characteristics of the R-CDM database. RESULTS: Information from 2801360 cases and 87203226 DICOM files was organized into two tables constituting the R-CDM. Information on imaging device and image resolution was recorded with more than 99.9% accuracy. Furthermore, OMOP-CDM and R-CDM were linked to efficiently extract specific types of images from specific patient cohorts. CONCLUSION: R-CDM standardizes the structure and terminology for recording medical imaging data to eliminate incomplete and unstandardized information. Successful standardization was achieved by the extract, transform, and load process and image classifier. We hope that the R-CDM will contribute to deep learning research in the medical imaging field by enabling the securement of large-scale medical imaging data from multinational institutions.

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.

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.

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.