Simulating federated learning for steatosis detection using ultrasound images.

We aimed to implement four data partitioning strategies evaluated with four federated learning (FL) algorithms and investigate the impact of data distribution on FL model performance in detecting steatosis using B-mode US images. A private dataset (153 patients; 1530 images) and a public dataset (55 patient; 550 images) were included in this retrospective study. The datasets contained patients with metabolic dysfunction-associated fatty liver disease (MAFLD) with biopsy-proven steatosis grades and control individuals without steatosis.

Automated screening of potential organ donors using a temporal machine learning model.

Organ donation is not meeting demand, and yet 30-60% of potential donors are potentially not identified. Current systems rely on manual identification and referral to an Organ Donation Organization (ODO). We hypothesized that developing an automated screening system based on machine learning could reduce the proportion of missed potentially eligible organ donors.

Association between obesity and hospital mortality in critical COVID-19: a retrospective cohort study.

Background: The impact of obesity on outcomes in acute respiratory distress syndrome (ARDS) is not well understood and remains controversial. Recent studies suggest that obesity might be associated with higher morbidity and mortality in respiratory disease caused by SARS-CoV-2 (COVID-19 disease). Our objective was to evaluate the association between obesity and hospital mortality in critical COVID-19 patients.