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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.

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.To evaluate the regional analysis of LSN and processing time in a single axial liver MR image for staging liver fibrosis.Retrospective.A total of 210 subjects, a multicenter study.A 3 T/noncontrast gradient echo T1WI.Subjects were divided into five fibrosis groups (FRegional 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.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, ROIQuantitative regional LSN measurement in a single axial MR image reduces processing time. Regional ROI3 TECHNICAL EFFICACY: Stage 2.

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.

In March 2020, the World Health Organization declared the coronavirus disease (COVID-19) outbreak as a pandemic due to its uncontrolled global spread. Reverse transcription polymerase chain reaction is a laboratory test that is widely used for the diagnosis of this deadly disease. However, the limited availability of testing kits and qualified staff and the drastically increasing number of cases have hampered massive testing. To handle COVID19 testing problems, we apply the Internet of Things and artificial intelligence to achieve self-adaptive, secure, and fast resource allocation, real-time tracking, remote screening, and patient monitoring. In addition, we implement a cloud platform for efficient spectrum utilization. Thus, we propose a cloud based intelligent system for remote COVID-19 screening using cognitive radio-based Internet of Things and deep learning. Specifically, a deep learning technique recognizes radiographic patterns in chest computed tomography (CT) scans. To this end, contrast-limited adaptive histogram equalization is applied to an input CT scan followed by bilateral filtering to enhance the spatial quality. The image quality assessment of the CT scan is performed using the blind/referenceless image spatial quality evaluator. Then, a deep transfer learning model, VGG-16, is trained to diagnose a suspected CT scan as either COVID-19 positive or negative. Experimental results demonstrate that the proposed VGG-16 model outperforms existing COVID-19 screening models regarding accuracy, sensitivity, and specificity. The results obtained from the proposed system can be verified by doctors and sent to remote places through the Internet.

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.

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.

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.

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.

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.

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.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.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.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.

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.

The Observational Medical Outcomes Partnership-Common Data Model (OMOP-CDM) used in distributed research networks has low coverage of clinical data and does not reflect the latest trends of precision medicine. Radiology data have great merits to visualize and identify the lesions in specific diseases. However, radiology data should be shared to obtain the sufficient scale and diversity required to provide strong evidence for improving patient care. Our study was to develop a web-based management system for radiology-CDM (R-CDM), as an extension of the OMOP-CDM, and to assess the feasibility of an R-CDM dataset for application of radiological image data in AI learning. This study standardized a cirrhosis of liver (LC) R-CDM dataset consisting of CT data (LC 40,575 images vs. non-LC 33,565 images). With use of modified AI learning algorithm, the diagnostic accuracy was 0.99292 (error rate = 0.00708), and its sensitivity was 0.99469 for LC and specificity was 0.99115 for non-LC. We developed a web-based management system for searching and downloading standardized R-CDM dataset and constructed a liver cirrhosis R-CDM dataset for clinical practice. Our management system and LC dataset would be helpful for multicenter study and AI learning research.

Recently, there have been considerable efforts to develop non-invasive image-based method for diagnosis, staging, and monitoring of chronic liver disease (CLD). This study developed a deep learning (DL) algorithm for discriminating the CLD using non-contrast abdominal CT images. This study enrolled 499 patients with CLD and 122 healthy controls. The main structure of DL algorithm used GoogLeNet-V3 and several modules fine-tuned for this study. In the test data sets, the DL algorithm had a discrimination accuracy of 99.4% (loss is about 0.7%) and an AUROC of 0.998, for diagnosis between normal controls and CLD patients. In the validation test, we achieve good validation result as follows: specificity (for healthy controls) 0.98292 (error rate: 0.01708) and sensitivity (for CLD) 0.99469 (error rate: 0.00531), respectively. Our deep learning algorithm would be useful for accurate discrimination in CLD from the abdominal CT images without the use of contrast agent. Further study is needed to diagnose the disease severity within CLD patient group for clinical application.

Recent advances in artificial intelligence have been used for a variety of clinical interests, such as lesion detection, segmentation, classification, and differentiation in medical images, and a great deal of research is underway. A good algorithm is also needed to obtain meaningful artificial intelligence research results, but a correctly created data set is also important. To provide these data sets, we proposed the radiology-common data model (R-CDM), as an expansion of OMOP-CDM. With the use of R-CDM, this study created a data set to distinguish urinary diseases for 873 patients and showed significance using an artificial intelligence algorithm that modified GoogleNet, a deep-learning algorithm based on CNN. In this experiment, 99% accuracy and AUROC 0.9 were obtained. External validation was performed using urinary stones data sets for 200 patients at other hospitals to increase confidence in the results of deep learning. In external validation, the results of 90% sensitivity, 97% specificity, and AUROC 0.984 confirm that the data sets and algorithms have very high efficiency. The study will help researchers who perform artificial intelligence through medical imaging.

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

Digital chest tomosynthesis that provides a reconstructed 3D chest image is a superior technique to detect chest diseases. As it is difficult to detect diseases like lung cancer with conventional 2D digital chest X-ray technology (CXT), digital chest tomosynthesis improves upon the many of the limitations inherent in the 2D digital CXT. In this study, we report a digital chest tomosynthesis system (D-CTS) that can generate multi X-ray information for the reconstruction of a 3D Xray chest image. The D-CTS reported herein employs an array of carbon nanotube (CNT) emitter-based cold cathode electron-guns that are triggered in sequence to provide a gantry-less system (Figure 1). The CNTs are achieved by direct growth on a metal substrate and have a spaghetti-like structure (Figure 2) with fast response to electrical bias under vacuum conditions. Unlike conventional rotating type systems with gantries, our CTS has the advantage of less motion blur in image acquisition, given its stationary position. Additionally, the switching from one electron-gun (e-gun) to the next is much faster than the speed of conventional gantries, allowing faster acquisition time t required for digital operation. This system shows outstanding field emission property for taking X-ray images. The design, fabrication process and imaging processing of the multi-beam CNT X-ray system will be discussed during the presentation.

In this paper, we propose a skin care management support system that can provide easy intercommunication between patients and medical staff for optimal management of skin treatment and an aid to diagnosis. This system provides self-management and treatment advice for patients from medical staff. Our research is specially focused on the management of conditions such as psoriasis and melanoma. Although there exist systems have been developed for various medical applications, including some that provide self-monitoring via smartphones, the patient participation rate in using these applications has been low after initial use. This is because obtaining useful information regarding the diagnosis and treatment of disease without the support of medical staff is difficult. We propose a skin care management support system with an enhanced interaction method. It leverages a data synchronization mechanism to enable patients and medical staff to view simultaneously. The system environment is based on cloud computing environment, which provides secure communication by using an Advanced Encryption Standard (AES) between patients and medical staff. Finally, we demonstrate the complete skin care management procedure for skin diseases using a smartphone-based portal service.

In this study, we obtained a pulsatile photoplethysmogram (PPG) signal from a fingertip using the built-in camera of an iPhone 6 s and displayed a real-time heart activity images with holographic projection on a smartphone screen. Our proposed heart activity hologram is simple and can be easily realized with only a smartphone and overhead projector (OHP) film. A square pyramid-shaped OHP film was positioned on a smartphone screen. The actual cardiac cycle on the smartphone screen was projected onto the film while measuring the pulsatile signal from a fingertip placed on the smartphone’s camera. The heart’s beat-to-beat time interval was then recorded. This approach enables observation of one’s own virtual heart activity in real time, rather than seeing a pulsatile signal graphically on a smartphone. To investigate the feasibility of this heart activity monitoring based on holographic projection, we tested it under different conditions in terms of environmental light, smartphone screen light intensity, and film color, and quantified the contrast-to-noise ratio for comparison.

Atrial fibrillation (AF) and Congestive heart failure (CHF) are increasingly widespread, costly, deadly diseases and are associated with significant morbidity and mortality. In this study, we analyzed three statistical methods for automatic detection of AF and CHF based on the randomness, variability and complexity of the heart beat interval, which is RRI time series. Specifically, we used short RRI time series with 16 beats and employed the normalized root mean square of successive RR differences (RMSSD), the sample entropy and the Shannon entropy. The detection performance was analyzed using four large well documented databases, namely the MIT-BIH Atrial fibrillation (n =23), the MIT-BIH Normal Sinus Rhythm (n =18), the BIDMC Congestive Heart Failure (n =13) and the Congestive Heart Failure RRI databases (n =25). Using thresholds by Receiver Operating Characteristic (ROC) curves, we found that the normalized RMSSD provided the highest accuracy. The overall sensitivity, specificity and accuracy for AF and CHF were 0.8649, 0.9331 and 0.9104, respectively. Regarding CHF detection, the detection rate of CHF (NYHA III-IV) was 0.9113 while CHF (NYHA I-II) was 0.7312, which shows that the detection rate of CHF with higher severity is higher than that of CHF with lower severity. For the clinical 24 hour data (n =42), the overall sensitivity, specificity and accuracy for AF and CHF were 0.8809, 0.9406 and 0.9108, respectively, using normalized RMSSD.