Generative adversarial networks with fully connected layers to denoise PPG signals.

The detection of arterial pulsating signals at the skin periphery with Photoplethysmography (PPG) are easily distorted by motion artifacts. This work explores the alternatives to the aid of PPG reconstruction with movement sensors (accelerometer and/or gyroscope) which to date have demonstrated the best pulsating signal reconstruction.A generative adversarial network with fully connected layers is proposed for the reconstruction of distorted PPG signals.

AI-based models to predict decompensation on traumatic brain injury patients.

Traumatic Brain Injury (TBI) is a form of brain injury caused by external forces, resulting in temporary or permanent impairment of brain function. Despite advancements in healthcare, TBI mortality rates can reach 30%-40% in severe cases. This study aims to assist clinical decision-making and enhance patient care for TBI-related complications by employing Artificial Intelligence (AI) methods and data-driven approaches to predict decompensation.

Histopoly: A serious game for teaching histology to 1st year veterinary students.

Histology is a preclinical subject transversal in medical, dental, and veterinary curricula. Classical teaching approaches in histology are often undermined by lower motivation and engagement of students, which may be addressed by innovative learning environments. Herein, we developed a serious game approach and compared it with a classical teaching style.

- a large-scale dataset of 3D medical shapes for computer vision.

Objectives: The shape is commonly used to describe the objects. State-of-the-art algorithms in medical imaging are predominantly diverging from computer vision, where voxel grids, meshes, point clouds, and implicit surface models are used. This is seen from the growing popularity of ShapeNet (51,300 models) and Princeton ModelNet (127,915 models).

CLARE-XR: explainable regression-based classification of chest radiographs with label embeddings.

An automatic system for pathology classification in chest X-ray scans needs more than predictive performance, since providing explanations is deemed essential for fostering end-user trust, improving decision-making, and regulatory compliance. CLARE-XR is a novel methodology that, when presented with an X-ray image, identifies the associated pathologies and provides explanations based on the presentation of similar cases. The diagnosis is achieved using a regression model that maps an image into a 2D latent space containing the reference coordinates of all findings.

Automated Assessment of Pelvic Longitudinal Rotation Using Computer Vision in Canine Hip Dysplasia Screening.

Canine hip dysplasia (CHD) screening relies on accurate positioning in the ventrodorsal hip extended (VDHE) view, as even mild pelvic rotation can affect CHD scoring and impact breeding decisions. This study aimed to assess the association between pelvic rotation and asymmetry in obturator foramina areas (AOFAs) and to develop a computer vision model for automated AOFA measurement. In the first part, 203 radiographs were analyzed to examine the relationship between pelvic rotation, assessed through asymmetry in iliac wing and obturator foramina widths (AOFWs), and AOFAs.

Technological Resources for Physical Rehabilitation in Cancer Patients Undergoing Chemotherapy: A Scoping Review.

: Cancer patients undergoing chemotherapy often face challenges that reduce their physical function and quality of life. Technological resources offer innovative solutions for physical rehabilitation, but the extent of their application in this context remains unclear. This scoping review aims to explore and map the various technological tools used to support physical rehabilitation in cancer patients during chemotherapy, focusing on their potential to improve outcomes and enhance patient care.

Distribution-based detection of radiographic changes in pneumonia patterns: A COVID-19 case study.

Although the classification of chest radiographs has long been an extensively researched topic, interest increased significantly with the onset of the COVID-19 pandemic. Existing results are promising; however, the radiological similarities between COVID-19 and other types of respiratory diseases limit the success of conventional image classification approaches that focus on single instances. This study proposes a novel perspective that conceptualizes COVID-19 pneumonia as a deviation from a normative distribution of typical pneumonia patterns.

Prospective Validation and Usability Evaluation of a Mobile Diagnostic App for Obstructive Sleep Apnea.

: Obstructive sleep apnea (OSA) classification relies on polysomnography (PSG) results. Current guidelines recommend the development of clinical prediction algorithms in screening prior to PSG. A recent intuitive and user-friendly tool (OSABayes), based on a Bayesian network model using six clinical variables, has been proposed to quantify the probability of OSA.

One-class classification with confound control for cognitive screening in older adults using gait, fingertapping, cognitive, and dual tasks.

Background And Objectives: Early detection of cognitive impairment is crucial for timely clinical interventions aimed at delaying progression to dementia. However, existing screening tools are not ideal for wide population screening. This study explores the potential of combining machine learning, specifically, one-class classification, with simpler and quicker motor-cognitive tasks to improve the early detection of cognitive impairment.

APAtizer: a tool for alternative polyadenylation analysis of RNA-Seq data.

Summary: APAtizer is a tool designed to analyze alternative polyadenylation events on RNA-sequencing data. The tool handles different file formats, including BAM, htseq, and DaPars bedGraph files. It provides a user-friendly interface that allows users to generate informative visualizations, including Volcano plots, heatmaps, and gene lists.

Phase-Shifted Fiber Bragg Grating by Selective Pitch Slicing.

This paper presents a new type of phase-shifted Fiber Bragg Grating (FBG): the sliced-FBG (SFBG). The fabrication process involves cutting a standard FBG inside its grating region. As a result, the last grating pitch is shorter than the others.

Tutorial on the Use of the Photon Diffusion Approximation for Fast Calculation of Tissue Optical Properties.

Computer simulations, which are performed at a single wavelength at a time, have been traditionally used to estimate the optical properties of tissues. The results of these simulations need to be interpolated. For a broadband estimation of tissue optical properties, the use of computer simulations becomes time consuming and computer demanding.

A survey on cell nuclei instance segmentation and classification: Leveraging context and attention.

Nuclear-derived morphological features and biomarkers provide relevant insights regarding the tumour microenvironment, while also allowing diagnosis and prognosis in specific cancer types. However, manually annotating nuclei from the gigapixel Haematoxylin and Eosin (H&E)-stained Whole Slide Images (WSIs) is a laborious and costly task, meaning automated algorithms for cell nuclei instance segmentation and classification could alleviate the workload of pathologists and clinical researchers and at the same time facilitate the automatic extraction of clinically interpretable features for artificial intelligence (AI) tools. But due to high intra- and inter-class variability of nuclei morphological and chromatic features, as well as H&E-stains susceptibility to artefacts, state-of-the-art algorithms cannot correctly detect and classify instances with the necessary performance.

Are Aptamer-Based Biosensors the Future of the Detection of the Human Gut Microbiome?-A Systematic Review and Meta-Analysis.

The gut microbiome is shaped early in life by dietary and lifestyle factors. Specific compounds in the gut affect the growth of different bacterial species and the production of beneficial or harmful byproducts. Dysbiosis of the gut microbiome has been linked to various diseases resulting from the presence of harmful bacteria and their byproducts.

Calibration and Modeling of the Semmes-Weinstein Monofilament for Diabetic Foot Management.

Diabetic foot is a serious complication that poses significant risks for diabetic patients. The resulting reduction in protective sensitivity in the plantar region requires early detection to prevent ulceration and ultimately amputation. The primary method employed for evaluating this sensitivity loss is the 10 gf Semmes-Weinstein monofilament test, commonly used as a first-line procedure.

UAV Visual and Thermographic Power Line Detection Using Deep Learning.

Inspecting and maintaining power lines is essential for ensuring the safety, reliability, and efficiency of electrical infrastructure. This process involves regular assessment to identify hazards such as damaged wires, corrosion, or vegetation encroachment, followed by timely maintenance to prevent accidents and power outages. By conducting routine inspections and maintenance, utilities can comply with regulations, enhance operational efficiency, and extend the lifespan of power lines and equipment.

Ethical debates amidst flawed healthcare artificial intelligence metrics.

Healthcare AI faces an ethical dilemma between selective and equitable deployment, exacerbated by flawed performance metrics. These metrics inadequately capture real-world complexities and biases, leading to premature assertions of effectiveness. Improved evaluation practices, including continuous monitoring and silent evaluation periods, are crucial.

Plasmonic nanoparticle sensors: current progress, challenges, and future prospects.

Plasmonic nanoparticles (NPs) have played a significant role in the evolution of modern nanoscience and nanotechnology in terms of colloidal synthesis, general understanding of nanocrystal growth mechanisms, and their impact in a wide range of applications. They exhibit strong visible colors due to localized surface plasmon resonance (LSPR) that depends on their size, shape, composition, and the surrounding dielectric environment. Under resonant excitation, the LSPR of plasmonic NPs leads to a strong field enhancement near their surfaces and thus enhances various light-matter interactions.

A Clinician's Guide to Understanding Bias in Critical Clinical Prediction Models.

This narrative review focuses on the role of clinical prediction models in supporting informed decision-making in critical care, emphasizing their 2 forms: traditional scores and artificial intelligence (AI)-based models. Acknowledging the potential for both types to embed biases, the authors underscore the importance of critical appraisal to increase our trust in models. The authors outline recommendations and critical care examples to manage risk of bias in AI models.