'Eye-Tracking' with Words for Alzheimer's Disease Detection: Time Alignment of Words Enunciation with Image Regions During Image Description Tasks.

Background: Alzheimer's disease (AD) is a progressive neurodegenerative disease that results in cognitive decline, dementia, and eventually death. Diagnosing early signs of AD can help clinicians to improve the quality of life.

Objective: We developed a non-invasive approach to help neurologists and clinicians to distinguish probable AD patients and healthy controls (HC).

Automated detection of regions of interest in cartridge case images using deep learning.

This paper explores a deep-learning approach to evaluate the position of circular delimiters in cartridge case images. These delimiters define two regions of interest (ROI), corresponding to the breech face and the firing pin impressions, and are placed manually or by an image-processing algorithm. This positioning bears a significant impact on the performance of the image-matching algorithms for firearm identification, and an automated evaluation method would be beneficial to any computerized system.

Navigation guidance for ventricular septal defect closure in heart phantoms.

Purpose: Transesophageal echocardiography (TEE) is the preferred imaging modality in a hybrid procedure used to close ventricular septal defects (VSDs). However, the limited field of view of TEE hinders the maneuvering of surgical instruments inside the beating heart. This study evaluates the accuracy of a method that aims to support navigation guidance in the hybrid procedure.

Design of heart phantoms for ultrasound imaging of ventricular septal defects.

Purpose: Ventricular septal defects (VSDs) are common congenital heart malformations. Echocardiography used during VSD hybrid cardiac procedures requires extensive training for image acquisition and interpretation. Cardiac surgery simulators with heart phantoms have shown usefulness for such training, but they are limited in visualization and characterization of complex VSD.

Multilingual automation of transcript preprocessing in Alzheimer's disease detection.

Introduction: Analyzing linguistic functions can improve early detection of Alzheimer's disease (AD). To date, no studies have focused on creating a universal pipeline for clinical transcript preprocessing.

Methods: This article presents a simple and efficient method for processing linguistic and phonetic data, sequencing subproblems of cleaning, normalization, and measure extraction tasks.

Cluster Analysis of Obesity Disease Based on Comorbidities Extracted from Clinical Notes.

Clinical notes provide a comprehensive and overall impression of the patient's health. However, the automatic extraction of information within these notes is challenging due to their narrative style. In this context, our goal was to identify clusters of patients based on fourteen comorbidities related to obesity, automatically extracted with the cTAKES tool from the i2b2 Obesity Challenge data.

Comparison of MetaMap and cTAKES for entity extraction in clinical notes.

Background: Clinical notes such as discharge summaries have a semi- or unstructured format. These documents contain information about diseases, treatments, drugs, etc. Extracting meaningful information from them becomes challenging due to their narrative format.

Computer-based evaluation of Alzheimer's disease and mild cognitive impairment patients during a picture description task.

Introduction: We present a methodology to automatically evaluate the performance of patients during picture description tasks.

Methods: Transcriptions and audio recordings of the Cookie Theft picture description task were used. With 25 healthy elderly control (HC) samples and an information coverage measure, we automatically generated a population-specific referent.