Usefulness of day-one chest X-ray score for predicting mortality and ICU admission in COVID-19 patients

We aimed to investigate the performance of a chest X-ray (CXR) scoring scale of lung injury in prediction of death and ICU admission among patients with COVID-19 admitted at Vinmec Central Park hospital (HCM City, VN) during the peak epidemic in 2021. X-ray images and clinical data were collected from patients with SARS-CoV-2 PCR positive from July to September 2021. Three radiologists independently assessed the CXR score at admission which is the sum of severity and extent of lung injuries on four lung quadrants (maximum score = 24). Among 219 patients included, 28 died including 25 from 34 patients admitted to the ICU. There was a high consensus for CXR scoring among radiologists (kappa = 0.90;CI95%: 0.89-0.92). CXR score was the strongest predictor of mortality (tdAUC 0.85;CI95%: 0.69-1) within the first 3 weeks after admission. Multivariate model with adjustment for age confirmed a significant effect of increased CXR score on mortality risk (HR = 1.33, CI95%: 1.10 to 1.62). At a threshold of 16 points, the CXR score allows predicting in-hospital mortality and ICU admission with good sensitivity (0.82 (CI95%: 0.78 to 0.87) and 0.86 (CI95%: 0.81 to 0.90)) and specificity (0.89 (CI95%: 0.88 to 0.90) and 0.87 (CI95%: 0.86 to 0.89), respectively). The day-one CXR score is a reliable predictor of the risk of death and ICU admission and could be used to identify high-risk patients in needy countries like Vietnam.

An IoT Solution for Multiple Sensors Control and Management

The era of the fourth industrial revolution and the Covid-19 pandemic gives rise to the incredible growth of the IoT (Internet of Things) field. The trend of an immense amount of devices connected to specific networks brings up various problems, but most noticeable, control and management issues. In this paper, we propose a structural and efficient solution to solve this problem in a large-scale IoT system - along with two implementations of the proposed solution. The paper's main contribution is to present an overview of the architecture for an IoT system inspired by numerous IoT-based implementations. The design is expected to be dynamic, transparent, and easily deployed for newcomers. Furthermore, with the different implementations in health care and agriculture mentioned later, we want to demonstrate the flexibility and adaptability of the design to various fields. © 2022 IEEE.

VQC-COVID-NET: Vector Quantization Contrastive Learning for Covid-19 Image Base Classification

Today, the COVID-19 epidemic has become extremely widespread. The first step in combating COVID-19 is identifying cases of infection. Real-time reverse transcriptase polymerase chain reaction is the most common method for identifying COVID (RT-PCR). This method, however, has been compromised by a time-consuming, laborious, and complex manual process. In addition to the RT-PCR test, screening computed tomography scan (CT) or X-ray images may be used to identify positive COVID-19 results, which could aid in the detection of COVID-19. Because of the continuing increase in new infections, the development of automated techniques for COVID-19 detection utilizing CT images is in high demand. This will aid in clinical diagnosis and alleviate the arduous task of image interpretation. Aggregating instances from various medical systems is highly advantageous for enlarging datasets for the development of machine learning techniques and the acquisition of robust, generalizable models. This study proposes a novel method for addressing distinct feature normalization in latent space due to cross-site domain shift in order to accurately execute COVID-19 identification using heterogeneous datasets with distribution disagreement. We propose using vector quantization to enhance the domain invariance of semantic embeddings in order to enhance classification performance on each dataset. We use two large, publicly accessible COVID-19 diagnostic CT scan datasets to develop and validate our proposed model. The experimental results demonstrate that our proposed method routinely outperforms state-of-the-art techniques on testing datasets. Public access to the implementation of our proposed method is available at https://github.com/khaclinh/VQC-COVID-NET. © 2022 IEEE.

Adaptations of Breast Imaging Centers to the COVID-19 Pandemic: A Survey of California and Texas

Objective: To determine the early impact of the COVID-19 pandemic on breast imaging centers in California and Texas and compare regional differences. Methods: An 11-item survey was emailed to American College of Radiology accredited breast imaging facilities in California and Texas in August 2020. A question subset addressed March-April government restrictions on elective services ("during the shutdown" and "after reopening"). Comparisons were made between states with chi-square and Fisher's tests, and timeframes with McNemar's and paired t-tests. Results: There were 54 respondents (54/240, 23%, 26 California, 28 Texas). Imaging volumes fell during the shutdown and remained below pre-pandemic levels after reopening, with reduction in screening greatest (ultrasound 12% of baseline, mammography 13%, MRI 23%), followed by diagnostic MRI (43%), procedures (44%), and diagnostics (45%). California reported higher volumes during the shutdown (procedures, MRI) and after reopening (diagnostics, procedures, MRI) versus Texas (P=0.001-0.02). Most screened patients (52/54, 96% symptoms and 42/54, 78% temperatures), and 100% (53/53) modified check-in and check-out. Reading rooms or physician work were altered for social distancing (31/54, 57%). Physician mask (45/48, 94%), gown (15/48, 31%), eyewear (22/48, 46%), and face shield (22/48, 46%) use during procedures increased after reopening versus pre-pandemic (P<0.001-0.03). Physician (47/54, 87%) and staff (45/53, 85%) financial impacts were common, but none reported terminations. Conclusion: Breast imaging volumes during the early pandemic fell more severely in Texas than in California. Safety measures and financial impacts on physicians and staff were similar in both states.

An algorithm in ophthalmic emergencies to evaluate the necessity of physical consultation during COVID-19 lockdown in Paris: Experience of the first 100 patients.

PURPOSE: This study aimed to evaluate the ability of a freely accessible internet algorithm to correctly identify the need for emergency ophthalmologic consultation for correct diagnosis and management. METHOD: This retrospective observational cohort study was based on the first 100 patients who requested recommendations on the necessity of breaking the lockdown for emergency ophthalmology consultation during the period from March to May 2020. RESULTS: Ninety-one patients completed questionnaires. Forty-nine were directed to emergency consultation and 42 to differed scheduled visits or telemedicine visits. One patient sent for emergency consultation had an overestimated severity and could have been seen later, while two patients initially recommended for a scheduled visit were considered appropriate for emergency consultation. However, these patients' management did not suffer as a consequence of the delay. The sensitivity of the algorithm, defined as the number of emergency consultations suggested by the algorithm divided by the total number of emergency consultations deemed appropriate by the practitioner's final evaluation, was 96.0%. The specificity of the algorithm, defined as the number of patients recommended for delayed consultation by the algorithm divided by the number of patients deemed clinically appropriate for this approach, was 97.5%. The positive predictive value, defined as the number of appropriate emergency consultations divided by the total number of emergency consultations suggested by the algorithm, was 97.9%. Finally, the negative predictive value, defined as the number of appropriately deferred patients divided by the number of deferred patients recommended by the algorithm, was 95.2%. CONCLUSION: This study demonstrates the reliability of an algorithm based on patients' past medical history and symptoms to classify patients and direct them to either emergency consultation or to a more appropriate deferred, scheduled appointment. This algorithm might allow reduction of walk-in visits by half and thus help control patient flow into ophthalmologic emergency departments.