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Participant motion during functional magnetic resonance image (fMRI) acquisition produces spurious signal fluctuations that can confound measures of functional connectivity. Without mitigation, motion artifact can bias statistical inferences about relationships between connectivity and individual differences. To counteract motion artifact, this protocol describes the implementation of a validated, high-performance denoising strategy that combines a set of model features, including physiological signals, motion estimates, and mathematical expansions, to target both widespread and focal effects of subject movement. This protocol can be used to reduce motion-related variance to near zero in studies of functional connectivity, providing up to a 100-fold improvement over minimal-processing approaches in large datasets. Image denoising requires 40 min to 4 h of computing per image, depending on model specifications and data dimensionality. The protocol additionally includes instructions for assessing the performance of a denoising strategy. Associated software implements all denoising and diagnostic procedures, using a combination of established image-processing libraries and the eXtensible Connectivity Pipeline (XCP) software.

Diagnostic errors are costly and they can contribute to adverse patient outcomes, including avoidable deaths. Differential diagnosis (DDX) generators are electronic tools that may facilitate the diagnostic process. We conducted a systematic review and meta-analysis to investigate the efficacy and utility of DDX generators. We undertook a comprehensive search of the literature including 16 databases from inception to May 2015 and specialist patient safety databases. We also searched the reference lists of included studies. Article screening, selection and data extraction were independently conducted by 2 reviewers. 36 articles met the eligibility criteria and the pooled accurate diagnosis retrieval rate of DDX tools was high with high heterogeneity (pooled rate = 0.70, 95% CI = 0.63 to 0.77; I2 = 97%, p<0.0001). DDX generators did not demonstrate improved diagnostic retrieval compared to clinicians but small improvements were seen in the before and after studies where clinicians had the opportunity to revisit their diagnoses following DDX generator consultation. Clinical utility data generally indicated high levels of user satisfaction and significant reductions in time taken to use for newer web-based tools. Lengthy differential lists and their low relevance were areas of concern and have the potential to increase diagnostic uncertainty. Data on the number of investigations ordered and on cost-effectiveness remain inconclusive. DDX generators have the potential to improve diagnostic practice among clinicians. However, the high levels of heterogeneity, the variable quality of the reported data and the minimal benefits observed for complex cases suggest caution. Further research needs to be undertaken in routine clinical settings with greater consideration of enablers and barriers which are likely to impact on DDX use before their use in routine clinical practice can be recommended.

Variations in paraspinal muscle cross-sectional area (CSA) and composition, particularly of the multifidus muscle, have been of interest with respect to risk of, and recovery from, low back pain problems. Several investigators have reported on the reliability of such muscle measurements using various protocols and image analysis programs. However, there is no standard protocol for tissue segmentation, nor has there been an investigation of reliability or agreement of measurements using different software. The purpose of this study was to provide a detailed muscle measurement protocol and determine the reliability and agreement of associated paraspinal muscle composition measurements obtained with 2 commonly used image analysis programs: OsiriX and ImageJ. This was a measurement reliability study. Lumbar magnetic resonance images of 30 individuals were randomly selected from a cohort of patients with various low back conditions. Muscle CSA and composition measurements were acquired from axial T2-weighted magnetic resonance images of the multifidus muscle, the erector spinae muscle, and the 2 muscles combined at L4-L5 and S1 for each participant. All measurements were repeated twice using each software program, at least 5 days apart. The assessor was blinded to all earlier measurements. The intrarater reliability and standard error of measurement (SEM) were comparable for most measurements obtained using OsiriX or ImageJ, with reliability coefficients (intraclass correlation coefficients [ICCs]) varying between .77 and .99 for OsiriX and .78 and .99 for ImageJ. There was similarly excellent agreement between muscle composition measurements using the 2 software applications (inter-software ICCs = .81-.99). The high degree of inter-software measurement reliability may not generalize to protocols using other commercial or custom-made software. The proposed method to investigate paraspinal muscle CSA, composition, and side-to-side asymmetry was highly reliable, with excellent agreement between the 2 software programs.

The novel coronavirus 2019 (COVID-2019), which first appeared in Wuhan city of China in December 2019, spread rapidly around the world and became a pandemic. It has caused a devastating effect on both daily lives, public health, and the global economy. It is critical to detect the positive cases as early as possible so as to prevent the further spread of this epidemic and to quickly treat affected patients. The need for auxiliary diagnostic tools has increased as there are no accurate automated toolkits available. Recent findings obtained using radiology imaging techniques suggest that such images contain salient information about the COVID-19 virus. Application of advanced artificial intelligence (AI) techniques coupled with radiological imaging can be helpful for the accurate detection of this disease, and can also be assistive to overcome the problem of a lack of specialized physicians in remote villages. In this study, a new model for automatic COVID-19 detection using raw chest X-ray images is presented. The proposed model is developed to provide accurate diagnostics for binary classification (COVID vs. No-Findings) and multi-class classification (COVID vs. No-Findings vs. Pneumonia). Our model produced a classification accuracy of 98.08% for binary classes and 87.02% for multi-class cases. The DarkNet model was used in our study as a classifier for the you only look once (YOLO) real time object detection system. We implemented 17 convolutional layers and introduced different filtering on each layer. Our model (available at (https://github.com/muhammedtalo/COVID-19)) can be employed to assist radiologists in validating their initial screening, and can also be employed via cloud to immediately screen patients. [Display omitted] •Proposed deep model for early detection of COVID-19 cases using X-ray images.•Obtained accuracy of 98.08% and 87.02% for binary and multi-classes.•Proposed heatmaps can help the radiologists to locate the affected regions on chest X-rays.•DarkCovidNet model can assist the clinicians to make faster and accurate diagnosis.

Fast diagnostic methods can control and prevent the spread of pandemic diseases like coronavirus disease 2019 (COVID-19) and assist physicians to better manage patients in high workload conditions. Although a laboratory test is the current routine diagnostic tool, it is time-consuming, imposing a high cost and requiring a well-equipped laboratory for analysis. Computed tomography (CT) has thus far become a fast method to diagnose patients with COVID-19. However, the performance of radiologists in diagnosis of COVID-19 was moderate. Accordingly, additional investigations are needed to improve the performance in diagnosing COVID-19. In this study is suggested a rapid and valid method for COVID-19 diagnosis using an artificial intelligence technique based. 1020 CT slices from 108 patients with laboratory proven COVID-19 (the COVID-19 group) and 86 patients with other atypical and viral pneumonia diseases (the non-COVID-19 group) were included. Ten well-known convolutional neural networks were used to distinguish infection of COVID-19 from non-COVID-19 groups: AlexNet, VGG-16, VGG-19, SqueezeNet, GoogleNet, MobileNet-V2, ResNet-18, ResNet-50, ResNet-101, and Xception. Among all networks, the best performance was achieved by ResNet-101 and Xception. ResNet-101 could distinguish COVID-19 from non-COVID-19 cases with an AUC of 0.994 (sensitivity, 100%; specificity, 99.02%; accuracy, 99.51%). Xception achieved an AUC of 0.994 (sensitivity, 98.04%; specificity, 100%; accuracy, 99.02%). However, the performance of the radiologist was moderate with an AUC of 0.873 (sensitivity, 89.21%; specificity, 83.33%; accuracy, 86.27%). ResNet-101 can be considered as a high sensitivity model to characterize and diagnose COVID-19 infections, and can be used as an adjuvant tool in radiology departments. •Ten CNNs were used to distinguish infection of COVID-19 from non-COVID-19 groups.•ResNet-101 and Xception represented the best performance with an AUC of 0.994.•Deep learning technique can be used as an adjuvant tool in diagnosing COVID-19.

Chest CT is emerging as a valuable diagnostic tool for clinical management of COVID-19 associated lung disease. Artificial intelligence (AI) has the potential to aid in rapid evaluation of CT scans for differentiation of COVID-19 findings from other clinical entities. Here we show that a series of deep learning algorithms, trained in a diverse multinational cohort of 1280 patients to localize parietal pleura/lung parenchyma followed by classification of COVID-19 pneumonia, can achieve up to 90.8% accuracy, with 84% sensitivity and 93% specificity, as evaluated in an independent test set (not included in training and validation) of 1337 patients. Normal controls included chest CTs from oncology, emergency, and pneumonia-related indications. The false positive rate in 140 patients with laboratory confirmed other (non COVID-19) pneumonias was 10%. AI-based algorithms can readily identify CT scans with COVID-19 associated pneumonia, as well as distinguish non-COVID related pneumonias with high specificity in diverse patient populations. Chest CT is emerging as a valuable diagnostic tool for clinical management of COVID-19 associated lung disease. Here, the authors present a multinational study on the application of deep learning algorithms for COVID-19 diagnosis against multiple lung conditions as controls.

Extracellular vesicles (EVs) are phospholipid bilayer membrane-enclosed structures containing RNAs, proteins, lipids, metabolites, and other molecules, secreted by various cells into physiological fluids. EV-mediated transfer of biomolecules is a critical component of a variety of physiological and pathological processes. Potential applications of EVs in novel diagnostic and therapeutic strategies have brought increasing attention. However, EV research remains highly challenging due to the inherently complex biogenesis of EVs and their vast heterogeneity in size, composition, and origin. There is a need for the establishment of standardized methods that address EV heterogeneity and sources of pre-analytical and analytical variability in EV studies. Here, we review technologies developed for EV isolation and characterization and discuss paths toward standardization in EV research. Despite the substantial recent progress made in extracellular vesicle (EV) research, our understanding of the functional and mechanistic biology of EVs and their relevance to specific pathophysiological states remains limited.Detailed characterization of the molecular composition of EVs and EV subpopulations remains a challenge.Alternative, similar, or identical experimental approaches may often lead to substantially different EV profiling results in different laboratories.Standard protocols for specimen procurement, collection, preprocessing, EV isolation, analytical characterization, and data analysis/interpretation need to be developed for specialized applications and analytical workflows, optimized, documented, cross-evaluated by several laboratories, and disseminated to further accelerate progress toward further understanding of EV biology and development of novel EV-based diagnostic and therapeutic approaches.

The social-ecological systems framework (SESF) is arguably the most comprehensive conceptual framework for diagnosing interactions and outcomes in social-ecological systems (SES). This article systematically reviews the literature applying and developing the SESF and discusses methodological challenges for its continued use and development. Six types of research approaches using the SESF are identified, as well as the context of application, types of data used, and commonly associated concepts. The frequency of how each second-tier variable is used across articles is analyzed. A summary list of indicators used to measure each second-tier variable is provided. Articles suggesting modifications to the framework are summarized and linked to the specific variables. The discussion reflects on the results and focuses on methodological challenges for applying the framework. First, how the SESF is historically related to commons and collective action research. This affects its continued development in relation to inclusion criteria for variable modification and discourse in the literature. The framework may evolve into separate modified versions for specific resource use sectors (e.g., forestry, fisheries, food production, etc.), and a general framework would aggregate the generalizable commonalities between them. Methodological challenges for applying the SESF are discussed related to research design, transparency, and cross-case comparison. These are referred to as “methodological gaps” that allow the framework to be malleable to context but create transparency, comparability, and data abstraction issues. These include the variable-definition gap, variable-indicator gap, the indicator-measurement gap, and the data transformation gap. A benefit of the framework has been its ability to be malleable and multipurpose, bringing a welcomed pluralism of methods, data, and associated concepts. However, pluralism creates challenges for synthesis, data comparison, and mutually agreed-upon methods for modifications. Databases are a promising direction forward to help solve this problem. In conclusion, future research is discussed by reflecting on the different ways the SESF may continue to be a useful tool through (1) being a general but adaptable framework, (2) enabling comparison, and (3) as a diagnostic tool for theory building.

ObjectiveThe purpose of this article was to perform a systematic review and meta-analysis regarding the diagnostic test accuracy of chest CT for detecting coronavirus disease 2019 (COVID-19).MethodsPubMed, Embase, Web of Science, and CNKI were searched up to March 12, 2020. We included studies providing information regarding diagnostic test accuracy of chest CT for COVID-19 detection. The methodological quality was assessed using the Quality Assessment of Diagnostic Accuracy Studies-2 tool. Sensitivity and specificity were pooled.ResultsSixteen studies (n = 3186 patients) were included. The risks of bias in all studies were moderate in general. Pooled sensitivity was 92% (95% CI = 86–96%), and two studies reported specificity (25% [95% CI = 22–30%] and 33% [95% CI = 23–44%], respectively). There was substantial heterogeneity according to Cochran’s Q test (p < 0.01) and Higgins I2 heterogeneity index (96% for sensitivity). After dividing the studies into two groups based on the study site, we found that the sensitivity of chest CT was great in Wuhan (the most affected city by the epidemic) and the sensitivity values were very close to each other (97%, 96%, and 99%, respectively). In the regions other than Wuhan, the sensitivity varied from 61 to 98%.ConclusionChest CT offers the great sensitivity for detecting COVID-19, especially in a region with severe epidemic situation. However, the specificity is low. In the context of emergency disease control, chest CT provides a fast, convenient, and effective method to early recognize suspicious cases and might contribute to confine epidemic.Key Points• Chest CT has a high sensitivity for detecting COVID-19, especially in a region with severe epidemic, which is helpful to early recognize suspicious cases and might contribute to confine epidemic.

Suspected fractures are among the most common reasons for patients to visit emergency departments (EDs), and X-ray imaging is the primary diagnostic tool used by clinicians to assess patients for fractures. Missing a fracture in a radiograph often has severe consequences for patients, resulting in delayed treatment and poor recovery of function. Nevertheless, radiographs in emergency settings are often read out of necessity by emergency medicine clinicians who lack subspecialized expertise in orthopedics, and misdiagnosedfractures account forupwardof four of everyfivereported diagnostic errors in certain EDs. In this work, we developed a deep neural network to detect and localize fractures in radiographs. We trained it to accurately emulate the expertise of 18 senior subspecialized orthopedic surgeons by having them annotate 135,409 radiographs. We then ran a controlled experiment with emergency medicine clinicians to evaluate their ability to detect fractures in wrist radiographs with and without the assistance of the deep learning model. The average clinician’s sensitivity was 80.8% (95% CI, 76.7–84.1%) unaided and 91.5% (95% CI, 89.3–92.9%) aided, and specificity was 87.5% (95 CI, 85.3–89.5%) unaided and 93.9% (95% CI, 92.9–94.9%) aided. The average clinician experienced a relative reduction in misinterpretation rate of 47.0% (95% CI, 37.4–53.9%). The significant improvements in diagnostic accuracy that we observed in this study show that deep learning methods are a mechanism by which senior medical specialists can deliver their expertise to generalists on the front lines of medicine, thereby providing substantial improvements to patient care.