Deep Left Ventricular Motion Estimation Methods in Echocardiography: A Comparative Study.

Motion estimation in echocardiography is critical when assessing heart function and calculating myocardial deformation indices. Nevertheless, there are limitations in clinical practice, particularly with regard to the accuracy and reliability of measurements retrieved from images. In this study, deep learning-based motion estimation architectures were used to determine the left ventricular longitudinal strain in echocardiography.

Improving Endoscopy Lesion Classification Using Self-Supervised Deep Learning.

In this work, we assess the impact of self-supervised learning (SSL) approaches on the detection of gastritis atrophy (GA) and intestinal metaplasia (IM) conditions. GA and IM are precancerous gastric lesions. Detecting these lesions is crucial to intervene early and prevent their progression to cancer.

Explainable Multimodal Deep Learning for Heart Sounds and Electrocardiogram Classification.

We introduce a Gradient-weighted Class Activation Mapping (Grad-CAM) methodology to assess the performance of five distinct models for binary classification (normal/abnormal) of synchronized heart sounds and electrocardiograms. The applied models comprise a one-dimensional convolutional neural network (1D-CNN) using solely ECG signals, a two-dimensional convolutional neural network (2D-CNN) applied separately to PCG and ECG signals, and two multimodal models that employ both signals. In the multimodal models, we implement two fusion approaches: an early fusion and a late fusion.

Characterization of work-related accidents among professionals in a public university from 2015 to 2019.

Introduction: Accidents at work are a critical reality in public institutions and, therefore, a highlight of attention and care.

Objectives: To characterize the profile of work-related accidents among professionals at a public university, from 2015 to 2019.

Methods: A quantitative, retrospective, and analytical study on work-related accidents recorded by a worker's health care sector at a public university in the interior of the state of Minas Gerais.