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Objective: In this study, we used artificial intelligence (AI) technology to explore for automated medical record quality control methods, standardize the process for medical record documentation, and deal with the drawbacks of manually implemented quality control. Methods: In this study, we constructed a medical record quality control system based on AI. We first designed and built, for the system, a quality control rule base based on authoritative standards and expert opinions. Then, medical records data were automatically collected through a data acquisition engine and were converted into structured data through a post-structured engine. Finally, the medical record quality control engine was combined with the rule base to analyze the data, identify quality problems, and realize automated intelligent quality control. This system was applied to the quality control of medical records and five quality control points were selected, including similarities in the history of the present illness, defects in the description of chief complaints, incomplete initial diagnosis, missing in formation in the history of menstruation, marriage, and childbirth, and mismatch between the chief complaints and the history of the present illness. We randomly selected 2 918 medical records of patients discharged in January 2022 to conduct AI quality control. Then we organized medical record quality control experts to conduct an accuracy review, made a comparison with previous manual quality control records, and analyzed the results. The number of quality problems that were verified in the accuracy review was taken as the gold standard and receiver operating characteristic (ROC) curves were drawn for the 5 quality control points. Results: According to the accuracy review performed by medical record quality control experts, the accuracy of AI quality control reached 89.57%. For the sampled medical records, the results of AI quality control were compared with those of previous manually performed quality control and only one problem detected by manual quality control of the sampled medical records was not detected by the AI quality control system. The number of medical record quality problems correctly detected by AI quality control was about 2.97 times that of manual quality control. Analysis of the ROC curves showed that the AUC of the five quality control points of the AI quality control system were statistically significant (P<0.05) and all the AUC values approximated or exceeded 0.9. In contrast, results obtained through manually performed quality control found significant AUC (0.797) for only one quality control point-similarities in the history of present illness (P<0.05). Comparison of the AUC values of the two quality control methods showed that AI quality control system had an advantage over manually performed quality control for the five quality control points. Conclusion: Through the application of medical record quality control system based on AI, efficient full quality control of medical record documentation can be achieved and the detection rate of quality problems can be effectively improved. In addition, the system helps save manpower and improve the quality of medical record documentation.

The value of diffusion weighted imaging-alberta stroke program early CT score in predicting stroke-associated pneumonia in patients with acute cerebral infarction: a retrospective study.

BACKGROUND: In this study, we aimed to investigate the value of Diffusion-Weighted Imaging-Alberta Stroke Program Early CT Score (DWI-ASPECTS) in predicting stroke-associated pneumonia (SAP) in patients with acute ischemic stroke. METHODS: A total of 291 patients who suffered acute cerebral infarction for the first time were included in this retrospective study. DWI-ASPECTS was assessed and clinical data were collected in order to find the risk factors of SAP, and a logistic regression model was used to investigate the effect of predicting SAP. Furthermore, correlation analysis was used to explore the relationship between DWI-ASPECTS and the immume status of the body. RESULTS: Among the 291 patients, 74 (25.4%) subjects were diagnosed with SAP. Compared with non-SAP, the patients with SAP were older and had a higher rate of atrial fibrillation (AF), National Institutes of Health Stroke Scale (NIHSS) scores. The SAP group also had a significantly lower DWI-ASPECTS than did the non-SAP group (P < 0.01). In the multivariable logistic regression analysis, the DWI-ASPECTS (adjusted odds ratio [aOR] = 1.438; 95% CI [1.158-1.787]; P < 0.01) remained significant after adjusting for confounders. What's more, the predictive ability of DWI-ASPECTS (AUC = 0.743 >0.7, 95% CI [0.678-0.800]) had acceptable discriminatory abilities. By the correlation analysis, DWI-ASPECTS was found to be negatively correlated with the count of white blood cell, neutrophils, monocytes, neutrophil-to-monocyte ratio and neutrophil-to-lymphocyte ratio, and positively correlated with the count of lymphocytes. CONCLUSIONS: DWI-ASPECTS grades could predict stroke-associated pneumonia for patients with acute ischemic stroke, and combining grade with age, AF, or NIHSS could predict SAP events more accurately.

Radiomics analysis enables fatal outcome prediction for hospitalized patients with coronavirus disease 2019 (COVID-19).

BACKGROUND: In December 2019, a rare respiratory disease named coronavirus disease 2019 (COVID-19) broke out, leading to great concern around the world. PURPOSE: To develop and validate a radiomics nomogram for predicting the fatal outcome of COVID-19 pneumonia. MATERIAL AND METHODS: The present study consisted of a training dataset (n = 66) and a validation dataset (n = 30) with COVID-19 from January 2020 to March 2020. A radiomics signature was generated using the least absolute shrinkage and selection operator (LASSO) Cox regression model. A radiomics score (Rad-score) was developed from the training cohort. The radiomics model, clinical model, and integrated model were built to assess the association between radiomics signature/clinical characteristics and the mortality of COVID-19 cases. The radiomics signature combined with the Rad-score and the independent clinical factors and radiomics nomogram were constructed. RESULTS: Seven stable radiomics features associated with the mortality of COVID-19 were finally selected. A radiomics nomogram was based on a combined model consisting of the radiomics signature and the clinical risk factors indicating optimal predictive performance for the fatal outcome of patients with COVID-19 with a C-index of 0.912 (95% confidence interval [CI] 0.867-0.957) in the training dataset and 0.907 (95% CI 0.849-0.966) in the validation dataset. The calibration curves indicated optimal consistency between the prediction and the observation in both training and validation cohorts. CONCLUSION: The CT-based radiomics nomogram indicated favorable predictive efficacy for the overall survival risk of patients with COVID-19, which could help clinicians intensively follow up high-risk patients and make timely diagnoses.

Surgical outcomes of two different reconstruction routes for esophagectomy in esophageal cancer patients: a meta-analysis.

To evaluate the effects of two different reconstruction routes (the posterior mediastinal route (PR) and the retrosternal route (RR)) on the surgical outcomes of patients after esophagectomy for esophageal carcinoma. PubMed, Embase, Web of Science and Scopus were searched from database inception to March 2021. Randomized controlled trials (RCTs) and case-control trials on the surgical outcomes of patients undergoing esophagectomy via one of the two routes were included. RevMan 5.3 software was used for the meta-analysis. In total, 19 studies were included, 8 were RCTs and 11 were case-control studies. The meta-analysis showed that among the case-control trials, the PR had reduced rates of anastomotic leakage [odds ratio (OR) = 0.56, 95% confidence interval (CI) (0.43, 0.74), P < 0.01]. In addition, it had reduced rates of anastomotic stenosis [OR = 0.42, 95% CI (0.30, 0.59), P < 0.01] and pulmonary complications [OR = 0.63, 95% CI (0.47, 0.84), P < 0.01]. However, there was no significant difference in cardiac complications [RCTs, relative risk (RR) = 0.57, 95% CI (0.29, 1.11), P = 0.10; case-control trials, OR = 1.06, 95% CI (0.70, 1.62), P = 0.78] or postoperative mortality [RCTs, RR = 0.47, 95% CI (0.19, 1.16), P = 0.10; case-control trials, OR = 0.68, 95% CI (0.32, 1.44), P = 0.31]. Compared with the RR, the PR had reduced rates of anastomotic leakage, anastomotic stenosis and pulmonary complications.

Magnetic Resonance Imaging (MRI) Differential Diagnosis of Meningiomas Using ANOVA.

This work explored the diagnostic value of different subtypes of meningiomas under T2WI low signal based on analysis of variance (ANOVA), and the expression differences of Ki67, VEGF, and P73 in different subtypes were analyzed. 67 patients with meningioma confirmed surgically and pathologically in hospital were selected as the research subjects, whose pathological classification occurs with obvious low signal on T2WI. First, the age distribution of the subjects and the distribution of different subtypes were counted. Then, ANOVA was adopted to analyze the MRI imaging signs of patients with different subtypes of meningioma. Finally, the differences of Ki67, VEGF, and P73 proteins and mRNA expression levels in different subtypes were detected via immunohistochemical assay and qPCR. The results showed that the proportion of patients with transitional meningioma was the most, which was 43.28%, while the proportion of patients with meningeal melanoma was the least, which was 7.46%. In patients with transitional meningioma, the MRI images showed mixed signals in different layers. Fibrous MRI images showed hyalinosis and calcification of collagen fibers in the tumor, with low T2WI signal. Sand-shape MRI images showed double low signals. MRI images of meningeal melanoma showed high signal on T1-weighted Imaging (T1WI) and low signal on T2WI. The protein expression and mRNA levels of Ki67 and P73 in transitional meningioma were evidently higher in contrast to those in fibrous meningioma (P < 0.05). The expression level of VEGF protein and mRNA in meningeal melanoma were notably higher in contrast to those in fibro meningioma (P < 0.05). It was revealed that the MRI images of the four subtypes of meningiomas under ANOVA-based T2WI low signal were quite different, and the expressions of Ki67, P73, and VEGF in different subtypes had significant differences. This work provided a reference basis for the preoperative diagnosis, treatment, and prognosis of meningiomas.

Development and multicenter validation of a CT-based radiomics signature for predicting severe COVID-19 pneumonia.

OBJECTIVES: To develop and validate a radiomics nomogram for timely predicting severe COVID-19 pneumonia. MATERIALS AND METHODS: Three hundred and sixteen COVID-19 patients (246 non-severe and 70 severe) were retrospectively collected from two institutions and allocated to training, validation, and testing cohorts. Radiomics features were extracted from chest CT images. Radiomics signature was constructed based on reproducible features using the least absolute shrinkage and selection operator (LASSO) logistic regression algorithm with 5-fold cross-validation. Logistic regression modeling was employed to build different models based on quantitative CT features, radiomics signature, clinical factors, and/or the former combined features. Nomogram performance for severe COVID-19 prediction was assessed with respect to calibration, discrimination, and clinical usefulness. RESULTS: Sixteen selected features were used to build the radiomics signature. The CT-based radiomics model showed good calibration and discrimination in the training cohort (AUC, 0.9; 95% CI, 0.843-0.942), the validation cohort (AUC, 0.878; 95% CI, 0.796-0.958), and the testing cohort (AUC, 0.842; 95% CI, 0.761-0.922). The CT-based radiomics model showed better discrimination capability (all p < 0.05) compared with the clinical factors joint quantitative CT model (AUC, 0.781; 95% CI, 0.708-0.843) in the training cohort, the validation cohort (AUC, 0.814; 95% CI, 0.703-0.897), and the testing cohort (AUC, 0.696; 95% CI, 0.581-0.796). Decision curve analysis demonstrated that in terms of clinical usefulness, the radiomics model outperformed the clinical factors model and quantitative CT model alone. CONCLUSIONS: The CT-based radiomics signature shows favorable predictive efficacy for severe COVID-19, which might assist clinicians in tailoring precise therapy. KEY POINTS: • Radiomics can be applied in CT images of COVID-19 and radiomics signature was an independent predictor of severe COVID-19. • CT-based radiomics model can predict severe COVID-19 with satisfactory accuracy compared with subjective CT findings and clinical factors. • Radiomics nomogram integrated with the radiomics signature, subjective CT findings, and clinical factors can achieve better severity prediction with improved diagnostic performance.

Evaluation of microvascular permeability of skeletal muscle and texture analysis based on DCE-MRI in alloxan-induced diabetic rabbits.

OBJECTIVES: To estimate the microvascular permeability and perfusion of skeletal muscle by using quantitative dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) and explore the feasibility of using texture analysis (TA) to evaluate subtle structural changes of diabetic muscles. METHODS: Twenty-four rabbits were randomly divided into diabetic (n = 14) and control (n = 10) groups, and underwent axial DCE-MRI of the multifidus muscle (0, 4, 8, 12, and 16 weeks after alloxan injection). The pharmacokinetic model was used to calculate the permeability parameters; texture parameters were extracted from volume transfer constant (Ktrans) map. The two-sample t test/Mann-Whitney U test, repeated measures analysis of variance/Friedman test, and Pearson correlations were used for data analysis. RESULTS: In the diabetic group, Ktrans and rate constant (Kep) increased significantly at week 8 and then showed a decreasing trend. Extravascular extracellular space volume fraction (Ve) increased and plasma volume fraction (Vp) decreased significantly from the 8th week. Skewness began to decrease at the 4th week. Median Ktrans and entropy increased significantly, while inverse difference moment decreased from the 8th week. Energy decreased while contrast increased only at week 8. Muscle fibre cross-sectional area was negatively correlated with Ve. The capillary-to-fibre ratio was positively correlated with Vp (p < 0.05, all). CONCLUSIONS: Quantitative DCE-MRI can be used to evaluate microvascular permeability and perfusion in diabetic skeletal muscle at an early stage; TA based on Ktrans map can identify microarchitectural modifications in diabetic muscles. KEY POINTS: • Four quantitative parameters of DCE-MRI can be used to evaluate microvascular permeability and perfusion of skeletal muscle in diabetic models at early stages. • Texture analysis based on Ktrans map can identify subtle structural changes in diabetic muscles.

High Resolution CT Imaging Dynamic Follow-Up Study of Novel Coronavirus Pneumonia.

Objective: To explore the clinical characteristics and dynamic follow-up changes of high resolution CT (HRCT) in 270 patients with Coronavirus Disease 2019 (COVID-19) pneumonia. Methods: Two hundred seventy COVID-19 pneumonia patients were retrospectively analyzed, including 146 males and 124 females, with median age of 51 (9,89). The clinical features, laboratory examination indexes and HRCT evolution findings of 270 COVID-19 pneumonia patients were analyzed. Results: 264 cases (95.74%) were positive at the first time nucleic acid test, 6 cases (2.22%) were negative, after multiple inspections, 270 cases (100%) were positive. According to the number of lung segments involved in the lesion, the lesions range from <30% of the lung area (Common type), 30-50% (Severe type), and> 50% (Critical type). At the first CT exam, 136 cases (50.37%) of the common type, 89 cases (32.96%) of the severe type and 45 cases (16.67%) of the critical type. At the second CT exam, 84 cases (31.11%) of the common type, 103 cases (38.15%) of the severe type and 83 cases (30.74%) of the critical type. In the third CT exam, there were 151 cases (55.93%) of the common type, 86 cases (31.85%) of the severe type and 33 cases (12.22%) of the critical type. The differences in image typing were statistically significant (P < 0.05). During this study, a total of 173 patients (64.08%) were recovered after treatment. Conclusion: In some epidemiological backgrounds, CT imaging manifestations and evolutionary characteristics are of great significance for early warning of lung injury, assessment of disease severity, and assistance in clinical typing and post-treatment follow-up.

A comparative study of diffusion kurtosis imaging and T2* mapping in quantitative detection of lumbar intervertebral disk degeneration.

PURPOSE: To assess the feasibility of diffusion kurtosis imaging (DKI) for diagnosing lumbar intervertebral disk degeneration (IDD) and to compare the potential of DKI and T2* mapping in the diagnosis of early IDD. METHODS: Sagittal T2WI, DKI, and T2* mapping were performed in 75 subjects with 375 lumbar intervertebral disks at a 3.0-T MRI. DKI-related parameters including mean kurtosis (MK), mean diffusivity (MD), fractional anisotropy (FA), and T2* values were calculated for each disk which was segmented into three regions: nucleus pulposus (NP), anterior annulus fibrosus (AAF), and posterior annulus fibrosus (PAF). RESULTS: MK and FA were positively correlated with Pfirrmann grade (all P < 0.001). MD and T2* were negatively correlated with Pfirrmann grade (all P < 0.001) except for T2* value of AAF (r = 0.087, P > 0.05). MK and FA values increased, while MD and T2* values decreased with age. No statistical significance was found between men and women (P > 0.05). Cephalic lumbar disks (L1/L2 and L2/L3) got lower MK and FA values than caudal lumbar disks (L4/L5 and L5/S1) (all P < 0.05), while cephalic lumbar disks got higher MD value than caudal lumbar disks (all P < 0.05). ROC analysis demonstrated that MK, MD, and FA showed significantly higher diagnostic accuracies than T2*, especially in NP and PAF. CONCLUSIONS: DKI can be used to assess human lumbar IDD. And DKI was more sensitive to the quantitative detection of early lumbar IDD than T2* mapping. These slides can be retrieved under Electronic Supplementary Material.

Altered whole-brain gray matter volume in primary angle closure glaucoma patients: a voxel-based morphometry study.

The study aimed to compare the whole-brain gray matter volume (GMV) and white matter volume (WMV) difference between primary angle closure glaucoma (PACG) patients and health controls (HCs) using a voxel-based morphometry method. A total of 27 patients with PACG (17 males and 10 females) and 27 HCs (17 males and 10 females), closely matched for age and education, were enrolled in the study. All subjects underwent magnetic resonance imaging (MRI) scans. The MRI data were processed using SPM8 software in voxel-based morphometry 8 toolbox. The relationship between the mean GMV values of brain regions and the clinical features including psychological testing and mean retinal nerve fiber layer (RNFL) thickness in PACG groups were analyzed by using Pearson correlation. Compared with HCs, PACG patients showed significantly decreased GMV values in the left cerebellum posterior lobe (CPL), right extra-nuclear, and right superior temporal gyrus. In contrast, PACG patients showed significantly increased GMV values in the left CPL, right CPL, right superior temporal gyrus, right thalamus and right insula (P<0.01). Moreover, in the PACG group, the left mean RNFL showed a positive correlation with the mean GMV values of the left CPL (r=0.719; P<0.001) and the right mean RNFL showed a positive correlation with the mean GMV values of the left CPL (r=0.721; P<0.001). The Hamilton depression score showed a positive correlation with the mean GMV values of right insula (r=0.897; P<0.001). Our results demonstrated that PACG patients showed altered brain structure in various regions related to visuomotor function, thalamocortical pathway, and emotion function, which might provide a useful informations to understanding the anatomy neural mechanisms of deficit in vision loss and depression in PACG.

Glioblastoma recurrence correlates with NLGN3 levels.

Glioblastoma (GBM) is the most aggressive glioma in the brain. Recurrence of GBM is almost inevitable within a short term after tumor resection. In a retrospective study of 386 cases of GBM collected between 2013 and 2016, we found that recurrence of GBM mainly occurs in the deep brain regions, including the basal ganglia, thalamus, and corpus callosum. But the mechanism underlying this phenomenon is not clear. Previous studies suggest that neuroligin-3 (NLGN3) is necessary for GBM growth. Our results show that the levels of NLGN3 in the cortex are higher than those in the deep regions in a normal human brain, and similar patterns are also found in a normal mouse brain. In contrast, NLGN3 levels in the deep brain regions of GBM patients are high. We also show that an increase in NLGN3 concentration promotes the growth of U251 cells and U87-MG cells. Respective use of the cortex neuron culture medium (C-NCM) and basal ganglia neuron culture medium (BG-NCM) with DMEM to cultivate U251, U87-MG and GBM cells isolated from patients, we found that these cells grew faster after treatment with C-NCM and BG-NCM in which the cells treated with C-NCM grew faster than the ones treated with BG-NCM group. Inhibition of NLGN3 release by ADAM10i prevents NCM-induced cell growth. Together, this study suggests that increased levels of NLGN3 in the deep brain region under the GBM pathological circumstances may contribute to GBM recurrence in the basal ganglia, thalamus, and corpus callosum.