Artificial Intelligence Assessment of Chest Radiographs for COVID-19.

Background: The sensitivity of reverse-transcription polymerase chain reaction (RT-PCR) is limited for diagnosis of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Chest computed tomography (CT) is reported to have high sensitivity; however, given the limited availability of chest CT during a pandemic, the assessment of more readily available imaging, such as chest radiographs, augmented by artificial intelligence may substitute for the detection of the features of coronavirus disease 2019 (COVID-19) pneumonia.

Methods: We trained a deep convolutional neural network to detect SARS-CoV-2 pneumonia using publicly available chest radiography imaging data including 8,851 normal, 6,045 pneumonia, and 200 COVID-19 pneumonia radiographs.

Single-center Outcomes After Liver Transplantation With SARS-CoV-2-Positive Donors: An Argument for Increased Utilization.

Background: The COVID-19 pandemic has led to an increase in SARS-CoV-2-test positive potential organ donors. The benefits of life-saving liver transplantation (LT) must be balanced against the potential risk of donor-derived viral transmission. Although emerging evidence suggests that the use of COVID-19-positive donor organs may be safe, granular series thoroughly evaluating safety are still needed.

Deep learning model for personalized prediction of positive MRSA culture using time-series electronic health records.

Methicillin-resistant Staphylococcus aureus (MRSA) poses significant morbidity and mortality in hospitals. Rapid, accurate risk stratification of MRSA is crucial for optimizing antibiotic therapy. Our study introduced a deep learning model, PyTorch_EHR, which leverages electronic health record (EHR) time-series data, including wide-variety patient specific data, to predict MRSA culture positivity within two weeks.