Background: To achieve imaging report standardization and improve the quality and efficiency of the intrainterdisciplinary clinical workflow, we proposed an intelligent imaging layout system (IILS) for a clinical decision support system-based ubiquitous healthcare service, which is a lung nodule management system using medical images. Methods: We created a lung IILS based on deep learning for imaging report standardization and workflow optimization for the identification of nodules. Our IILS utilized a deep learning plus adaptive auto layout tool, which trained and tested a neural network with imaging data from all the main CT manufacturers from 11,205 patients. Model performance was evaluated by the receiver operating characteristic curve (ROC) and calculating the corresponding area under the curve (AUC). The clinical application value for our IILS was assessed by a comprehensive comparison of multiple aspects. Findings: Our IILS is clinically applicable due to the consistency with nodules detected by IILS, with its highest consistency of 0•94 and an AUC of 90•6% for malignant pulmonary nodules versus benign nodules with a sensitivity of 76•5% and specificity of 89•1%. Applying this IILS to a dataset of chest CT images, we demonstrate performance comparable to that of human experts in providing a better layout and aiding in diagnosis in 100% valid images and nodule display. The IILS was superior to the traditional manual system in performance, such as reducing the number of clicks from 14•45 ± 0•38 to 2, time consumed from 16•87 ± 0•38 s to 6•92 ± 0•10 s, number of invalid images from 7•06 ± 0•24 to 0, and missing lung nodules from 46•8% to 0%. Interpretation: This IILS might achieve imaging report standardization, and improve the clinical workflow therefore opening a new window for clinical application of artificial intelligence. Fund: The National Natural Science Foundation of China.

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Background/objective Severe fever with thrombocytopenia syndrome (SFTS) cause encephalitis/encephalopathy, but few reports were available. We aimed to investigate the incidence of encephalitis/encephalopathy in SFTS patients and to summarize clinical characteristics, laboratory findings and imaging features. Methods We conducted a retrospective review of all patients with confirmed SFTS admitted to Nanjing Drum Tower Hospital, a tertiary hospital in Nanjing City, China, between January 2016 and July 2020. The patients were divided into two groups according to whether they had encephalitis/encephalopathy: encephalitis/encephalopathy group and non- encephalitis/encephalopathy group. Clinical data, laboratory findings, imaging characteristics, treatments and outcomes of these patients were collected and analyzed. Results A total of 109 SFTS patients with were included, of whom 30 (27.5 %) developed encephalitis/encephalopathy. In-hospital mortality (43.3 %) was higher in encephalitis/encephalopathy group than non-encephalitis/encephalopathy group (12.7 %). Univariate logistic regression showed that cough, wheezing, dyspnoea, respiratory failure, vasopressors use, bacteremia, invasive pulmonary aspergillosis (IPA) diagnoses, PCT > 0.5 ug/L, CRP > 8 mg/L, AST > 200 U/L and serum amylase level > 80 U/L were the risk factors for the development of encephalitis/encephalopathy for SFTS patients. Multivariate logistic regression analysis identified bacteremia, PCT > 0.5 mg/L and serum amylase level > 80 U/L as independent predictors of encephalitis/ encephalopathy development for SFTS patients. Conclusions SFTS-associated encephalitis/encephalopathy has high morbidity and mortality. it was necessary to strengthen the screening of CSF testing and brain imaging after admission for SFTS patients who had symptoms of encephalitis/encephalopathy. SFTS patients with bacteremia, PCT > 0.5 ug/L or serum amylase level > 80 U/L should be warned to progress to encephalopathy.

Purpose: Our objectives were to assess the abnormalities of subcortical nuclei by combining volume and shape analyses and potential association with cognitive impairment. Patients and Methods: Twenty-nine patients with severe ACS of the unilateral internal carotid artery and 31 controls were enrolled between January 2017 to August 2018. All participants underwent a comprehensive neuropsychological evaluation, blood lipid biochemical measurements, and structural magnetic resonance imaging (MRI) to measure subcortical volumes and sub-regional shape deformations. Basic statistics, correction for multiple comparisons. Seventeen ACS patients underwent carotid endarterectomy (CEA) within one week after baseline measurements, cognitive assessments and MRI scans were repeated 6 months after CEA. Results: The ACS patients had higher apolipoprotein B/apolipoprotein A1 (ApoB/ApoA1) ratio and worse performance in all cognitive domains than controls. Moreover, the ACS patients showed more profound thalamic atrophy assessed by shape and volume analysis, especially in the medial dorsal thalamus. No significant differences were found in other subcortical nuclei after multiple comparisons correction. At baseline, thalamic atrophy correlated with cognitive impairment and ApoB/ApoA1 ratio. Furthermore, mediation analysis at baseline showed that the association of carotid intima-media thickness with executive functioning was mediated by thalamic volume. After CEA, cognitive improvement and increase in the bilateral medial dorsal thalamic volume were observed. Conclusion: Our study identified the distinct atrophy of subcortical nuclei and their association with cognition in patients with ACS. Assessments of the thalamus by volumetric and shape analysis may provide an early marker for cerebral ischemia and reperfusion after CEA.

Background: This study aimed to build a radiomics model with deep learning (DL) and human auditing and examine its diagnostic value in differentiating between coronavirus disease 2019 (COVID-19) and community-acquired pneumonia (CAP).Methods: Forty-three COVID-19 patients, whose diagnoses had been confirmed with reverse-transcriptase polymerase-chain-reaction (RT-PCR) tests, and 60 CAP patients, whose diagnoses had been confirmed with sputum cultures, were enrolled in this retrospective study. The candidate regions of interest (ROIs) on the computed tomography (CT) images of the 103 patients were determined using a DL-based segmentation model powered by transfer learning. These ROIs were manually audited and corrected by 3 radiologists (with an average of 12 years of experience; range 6-17 years) to check the segmentation acceptance for the radiomics analysis. ROI-derived radiomics features were subsequently extracted to build the classification model and processed using 4 different algorithms (L1 regularization, Lasso, Ridge, and Z test) and 4 classifiers, including the logistic regression (LR), multi-layer perceptron (MLP), support vector machine (SVM), and extreme Gradient Boosting (XGboost). A receiver operating characteristic curve (ROC) analysis was conducted to evaluate the performance of the model.Results: Quantitative CT measurements derived from human-audited segmentation results showed that COVID-19 patients had significantly decreased numbers of infected lobes compared to patients in the CAP group {median [interquartile range (IQR)]: 4 (3, 4) and 4 (4, 5); P=0.031}. The infected percentage (%) of the whole lung was significantly more elevated in the CAP group [6.40 (2.77, 11.11)] than the COVID-19 group [1.83 (0.65, 4.42); P<0.001], and the same trend applied to each lobe, except for the superior lobe of the right lung [1.81 (0.09, 5.28) for COVID-19 vs. 1.32 (0.14, 7.02) for CAP; P=0.649]. Additionally, the highest proportion of infected lesions were observed in the CT value range of (-470, -370) Hounsfield units (HU) in the COVID-19 group. Conversely, the CAP group had a value range of (30, 60) HU. Radiomic model using corrected ROIs exhibited the highest area under ROC (AUC) of 0.990 [95% confidence interval (CI): 0.962 -1.000] using Lasso for feature selection and MLP for classification. Conclusions:The proposed radiomics model based on human-audited segmentation made accurate differential diagnoses of COVID-19 and CAP. The quantification of CT measurements derived from DL could potentially be used as effective biomarkers in current clinical practice.