COVID-Net CXR-S: Deep Convolutional Neural Network for Severity Assessment of COVID-19 Cases from Chest X-ray Images

The world is still struggling in controlling and containing the spread of the COVID-19 pandemic caused by the SARS-CoV-2 virus. The medical conditions associated with SARS-CoV-2 infections have resulted in a surge in the number of patients at clinics and hospitals, leading to a significantly increased strain on healthcare resources. As such, an important part of managing and handling patients with SARS-CoV-2 infections within the clinical workflow is severity assessment, which is often conducted with the use of chest X-ray (CXR) images. In this work, we introduce COVID-Net CXR-S, a convolutional neural network for predicting the airspace severity of a SARS-CoV-2 positive patient based on a CXR image of the patient’s chest. More specifically, we leveraged transfer learning to transfer representational knowledge gained from over 16,000 CXR images from a multinational cohort of over 15,000 SARS-CoV-2 positive and negative patient cases into a custom network architecture for severity assessment. Experimental results using the RSNA RICORD dataset showed that the proposed COVID-Net CXR-S has potential to be a powerful tool for computer-aided severity assessment of CXR images of COVID-19 positive patients. Furthermore, radiologist validation on select cases by two board-certified radiologists with over 10 and 19 years of experience, respectively, showed consistency between radiologist interpretation and critical factors leveraged by COVID-Net CXR-S for severity assessment. While not a production-ready solution, the ultimate goal for the open source release of COVID-Net CXR-S is to act as a catalyst for clinical scientists, machine learning researchers, as well as citizen scientists to develop innovative new clinical decision support solutions for helping clinicians around the world manage the continuing pandemic.

Comparison of Endotoxin Activity Assay and Various Biomarkers for Severity Assessment in Colorectal Perforation Patients

Colorectal perforation is a serious disease with high mortality requiring emergency surgery. This study aimed to evaluate the role of the endotoxin activity assay (EAA) to assess the severity in patients admitted to the intensive care unit after emergency surgeries for colorectal perforations. Patients were divided into high (EAA ≥.4) and low (EAA <.4) groups based on the EAA levels, and the correlation between the EAA values and clinical variables related to the severity was evaluated. The SOFA scores were significantly higher in the high group than those in the low group. The high EAA value persisted even after 48 hours and extended the ICU length of stay. These results suggest that EAA may be a potential biomarker to assess severity and useful as one of the instrumental in predicting the outcomes for colorectal perforation patients.

Acoustic emission corrosion feature extraction and severity prediction using hybrid wavelet packet transform and linear support vector classifier

Corrosion in carbon-steel pipelines leads to failure, which is a major cause of breakdown maintenance in the oil and gas industries. The acoustic emission (AE) signal is a reliable method for corrosion detection and classification in the modern Structural Health Monitoring (SHM) system. The efficiency of this system in detection and classification mainly depends on the suitable AE features. Therefore, many feature extraction and classification methods have been developed for corrosion detection and severity assessment. However, the extraction of appropriate AE features and classification of various levels of corrosion utilizing these extracted features are still challenging issues. To overcome these issues, this article proposes a hybrid machine learning approach that combines Wavelet Packet Transform (WPT) integrated with Fast Fourier Transform (FFT) for multiresolution feature extraction and Linear Support Vector Classifier (L-SVC) for predicting corrosion severity levels. A Laboratory-based Linear Polarization Resistance (LPR) test was performed on carbon-steel samples for AE data acquisition over a different time span. AE signals were collected at a high sampling rate with a sound well AE sensor using AEWin software. Simulation results show a linear relationship between the proposed approach-based extracted AE features and the corrosion process. For multi-class problems, three corrosion severity stages have been made based on the corrosion rate over time and AE activity. The ANOVA test results indicate the significance within and between the feature-groups where F-values (F-value>1) rejects the null hypothesis and P-values (P-value<0.05) are less than the significance level. The utilized L-SVC classifier achieves higher prediction accuracy of 99.0% than the accuracy of other benchmarked classifiers. Findings of our proposed machine learning approach confirm that it can be effectively utilized for corrosion detection and severity assessment in SHM applications.

Comparison of CT Severity Index and Modified CT Severity Index in the Clinical Severity Assessment of Acute Pancreatitis

Objective: To compare the computed tomography severity index (CTSI) and the modified computed tomography severity index (MCTSI) in the clinical severity assessment of acute pancreatitis.Material and Methods: This retrospective cohort study comprised acute pancreatitis patients who underwent contrastenhanced abdominal computed tomography (CT) scans within 4 weeks after clinical onset. Two experienced abdominal radiologists, blinded to the clinical outcome, independently reviewed the CT images and retrospectively scored them using CTSI and MCTSI. Any discrepancies were resolved by a consensus review. The clinical severity assessment of each participant was categorized by the determinant-based classification of acute pancreatitis severity. The correlations of CTSI and MCTSI with the clinical severity assessment were analyzed.Results: This cohort study consisted of 40 participants—28 of them were men (70.0%)—with a mean age of 59.3 years. They were clinically divided into mild, moderate, severe, and critical groups comprising 11 (27.5%), 16 (40.0%), 7 (17.5%), and 6 (15.0%) participants, respectively. Due to the small number of patients in the severe and critical groups, we combined these into a single severe category (13 patients, 32.5%). The CTSI and MCTSI scores showed moderate and fair agreement with the clinical severity assessment. A trend linking poor clinical outcome with high CTSI/MCTSI scores (moderate and severe groups) more commonly than low scores (mild group) was observed. There was a very strong agreement between CTSI and MCTSI (rs =0.97).Conclusion: CTSI and MCTSI showed a moderate and fair agreement, respectively, with the clinical severity assessment. Compared to low scores, a poor clinical outcome was more often associated with high CTSI/MCTSI scores.