ECG Marker Evaluation for the Machine-Learning-Based Classification of Acute and Chronic Phases of Infection in a Murine Model.

Chagas disease (CD) is a neglected parasitic disease caused by the protozoan (). The disease has two clinical phases: acute and chronic. In the acute phase, the parasite circulates in the blood.

Estimating Echocardiographic Myocardial Strain of Left Ventricle with Deep Learning.

The global longitudinal strain of the myocardial tissue has been shown to be a better indicator of cardiac pathologies in the subclinical stage than other indices, such as the ejection fraction. This article presents a new deep learning approach for strain estimation in 2D echocardiograms. The proposed method improves the performance of the state of the art without losing stability with noisy echocardiograms and achieved an average end point error of 0.

Neuromorphometry of primary brain tumors by magnetic resonance imaging.

Magnetic resonance imaging is a technique for the diagnosis and classification of brain tumors. Discrete compactness is a morphological feature of two-dimensional and three-dimensional objects. This measure determines the compactness of a discretized object depending on the sum of the areas of the connected voxels and has been used for understanding the morphology of nonbrain tumors.

Volumetric electromagnetic phase-shift spectroscopy of brain edema and hematoma.

Motivated by the need of poor and rural Mexico, where the population has limited access to advanced medical technology and services, we have developed a new paradigm for medical diagnostic based on the technology of "Volumetric Electromagnetic Phase Shift Spectroscopy" (VEPS), as an inexpensive partial substitute to medical imaging. VEPS, can detect changes in tissue properties inside the body through non-contact, multi-frequency electromagnetic measurements from the exterior of the body, and thereby provide rapid and inexpensive diagnostics in a way that is amenable for use in economically disadvantaged parts of the world. We describe the technology and report results from a limited pilot study with 46 healthy volunteers and eight patients with CT radiology confirmed brain edema and brain hematoma.

Computer assisted biopsy of breast tumors.

In this paper we report our preliminary results of the development of a computer assisted system for breast biopsy. The system is based on tracked ultrasound images of the breast. A three dimensional ultrasound volume is constructed from a set of tracked B-scan images acquired with a calibrated probe.

Prediction of infarct growth based on apparent diffusion coefficients: penumbral assessment without intravenous contrast material.

Purpose: To compare predicted and final infarct lesion volumes determined by processing apparent diffusion coefficient (ADC) maps derived at admission diffusion-weighted (DW) magnetic resonance (MR) imaging in patients with acute stroke and to verify that predicted areas of infarct growth reflect at-risk penumbral regions based on recanalization status.

Materials And Methods: The French legislation waived the requirement for informed patient consent for the described research, which was based on patient medical files. However, patients and/or their relatives were informed that they could decline to participate in the research.

Automatic prediction of infarct growth in acute ischemic stroke from MR apparent diffusion coefficient maps.

Rationale And Objectives: We introduce a new approach to the prediction of final infarct growth in human acute ischemic stroke based on image analysis of the apparent diffusion coefficient (ADC) maps obtained from magnetic resonance imaging. Evidence from multiple previous studies indicate that ADC maps are likely to reveal brain regions belonging to the ischemic penumbra, that is, areas that may be at risk of infarction in the few hours following stroke onset.

Materials And Methods: In a context where "time is brain," and contrarily to the alternative-and still-debated-perfusion-diffusion weighted image (PWI/DWI) mismatch approach, the DWI magnetic resonance sequences are standardized, fast to acquire, and do not necessitate injection of a contrast agent.

Robust nonparametric segmentation of infarct lesion from diffusion-weighted MR images.

Magnetic Resonance Imaging (MRI) is increasingly used for the diagnosis and monitoring of neurological disorders. In particular Diffusion-Weighted MRI (DWI) is highly sensitive in detecting early cerebral ischemic changes in acute stroke. Cerebral infarction lesion segmentation from DWI is accomplished in this work by applying nonparametric density estimation.