Exploring Components, Sensors, and Techniques for Cancer Detection via eNose Technology: A Systematic Review.

This paper offers a systematic review of advancements in electronic nose technologies for early cancer detection with a particular focus on the detection and analysis of volatile organic compounds present in biomarkers such as breath, urine, saliva, and blood. Our objective is to comprehensively explore how these biomarkers can serve as early indicators of various cancers, enhancing diagnostic precision and reducing invasiveness. A total of 120 studies published between 2018 and 2023 were examined through systematic mapping and literature review methodologies, employing the PICOS (Population, Intervention, Comparison, Outcome, and Study design) methodology to guide the analysis.

Automatic Segmentation of Quadriceps Femoris Cross-Sectional Area in Ultrasound Images: Development and Validation of Convolutional Neural Networks in People With Anterior Cruciate Ligament Injury and Surgery.

Objective: Deep learning approaches such as DeepACSA enable automated segmentation of muscle ultrasound cross-sectional area (CSA). Although they provide fast and accurate results, most are developed using data from healthy populations. The changes in muscle size and quality following anterior cruciate ligament (ACL) injury challenges the validity of these automated approaches in the ACL population.

Risk Perception and Knowledge Following a Social Game-Based Tobacco Prevention Program for Adolescents: Pilot Randomized Comparative Trial.

Background: Adolescence is a critical developmental stage that is particularly vulnerable to the initiation of tobacco use. Despite the well-documented health risks associated with tobacco use, it remains prevalent among adolescents. Games for health are a promising strategy for tobacco prevention, using experiential and social learning theories to enhance engagement and improve behavior change.

The art of selecting the ECG input in neural networks to classify heart diseases: a dual focus on maximizing information and reducing redundancy.

Background And Objectives: Accurate diagnosis of cardiovascular diseases often relies on the electrocardiogram (ECG). Since the cardiac vector is located within a three-dimensional space and the standard ECG comprises 12 projections or leads derived from it, redundant information is inherently present. This study aims to quantify this redundancy and its impact on classification tasks using Convolutional Neural Networks (CNNs) in cardiovascular diseases.