Fully automated epicardial adipose tissue volume quantification with deep learning and relationship with CAC score and micro/macrovascular complications in people living with type 2 diabetes: the multicenter EPIDIAB study.

Background: The aim of this study (EPIDIAB) was to assess the relationship between epicardial adipose tissue (EAT) and the micro and macrovascular complications (MVC) of type 2 diabetes (T2D).

Methods: EPIDIAB is a post hoc analysis from the AngioSafe T2D study, which is a multicentric study aimed at determining the safety of antihyperglycemic drugs on retina and including patients with T2D screened for diabetic retinopathy (DR) (n = 7200) and deeply phenotyped for MVC. Patients included who had undergone cardiac CT for CAC (Coronary Artery Calcium) scoring after inclusion (n = 1253) were tested with a validated deep learning segmentation pipeline for EAT volume quantification.

Left Ventricular Trabeculations at Cardiac MRI: Reference Ranges and Association with Cardiovascular Risk Factors in UK Biobank.

Background The extent of left ventricular (LV) trabeculation and its relationship with cardiovascular (CV) risk factors is unclear. Purpose To apply automated segmentation to UK Biobank cardiac MRI scans to () assess the association between individual characteristics and CV risk factors and trabeculated LV mass (LVM) and () establish normal reference ranges in a selected group of healthy UK Biobank participants. Materials and Methods In this cross-sectional secondary analysis, prospectively collected data from the UK Biobank (2006 to 2010) were retrospectively analyzed.

Finding the best trade-off between performance and interpretability in predicting hospital length of stay using structured and unstructured data.

Objective: This study aims to develop high-performing Machine Learning and Deep Learning models in predicting hospital length of stay (LOS) while enhancing interpretability. We compare performance and interpretability of models trained only on structured tabular data with models trained only on unstructured clinical text data, and on mixed data.

Methods: The structured data was used to train fourteen classical Machine Learning models including advanced ensemble trees, neural networks and k-nearest neighbors.

Machine-learning prediction for hospital length of stay using a French medico-administrative database.

Introduction: Prolonged Hospital Length of Stay (PLOS) is an indicator of deteriorated efficiency in Quality of Care. One goal of public health management is to reduce PLOS by identifying its most relevant predictors. The objective of this study is to explore Machine Learning (ML) models that best predict PLOS.

FDG-PET to T1 Weighted MRI Translation with 3D Elicit Generative Adversarial Network (E-GAN).

With the strengths of deep learning, computer-aided diagnosis (CAD) is a hot topic for researchers in medical image analysis. One of the main requirements for training a deep learning model is providing enough data for the network. However, in medical images, due to the difficulties of data collection and data privacy, finding an appropriate dataset (balanced, enough samples, etc.