Department of Engineering, University of Messina, Contrada di Dio, 98166 Messina, Italy.
Published: December 2024
AI Article Synopsis
Article Abstract
The knee is one of the joints most vulnerable to disease and injury, particularly in athletes and older adults. Surface temperature monitoring provides insights into the health of the analysed area, supporting early diagnosis and monitoring of conditions such as osteoarthritis and tendon injuries. This study presents an innovative approach that combines infrared thermography techniques with a Resnet 152 (DeepLabCut based) to detect and monitor temperature variations across specific knee regions during repeated sit-to-stand exercises. Thermal profiles are then analysed in relation to weight distribution data collected using a Wii Balance Board during the exercise. DeepLabCut was used to automate the selection of the region of interest (ROI) for temperature assessments, improving data accuracy compared to traditional time-consuming semi-automatic methods. This integrative approach enables precise and marker-free measurements, offering clinically relevant data that can aid in the diagnosis of knee pathologies, evaluation of the rehabilitation progress, and assessment of treatment effectiveness. The results emphasize the potential of combining thermography with DeepLabCut-driven data analysis to develop accessible, non-invasive tools for joint health monitoring or preventive diagnostics of pathologies.
Endovascular interventions are procedures designed to diagnose and treat vascular diseases, using catheters to navigate inside arteries and veins. Thanks to their minimal invasiveness, they offer many benefits, such as reduced pain and hospital stays, but also present many challenges for clinicians, as they require specialized training and heavy use of X-rays. This is particularly relevant when accessing (i.
Parkinson's disease (PD) prevalence is projected to reach 12 million by 2040. Wearable sensors offer a promising approach for comfortable, continuous tremor monitoring to optimize treatment strategies. Here, we present a wristwatch-like triboelectric sensor (WW-TES) inspired by automatic watches for unobtrusive PD tremor assessment.
Continuous glucose monitoring (CGM) has emerged as a superior method to glycated hemoglobin (HbA1c) monitoring for glycemic control assessment in type 1 diabetes (T1D). The association between CGM parameters and diabetic kidney disease (DKD) has not been extensively researched. The aim of the present study was to compare CGM metrics between patients with stable and progressive DKD and T1D.
Objective: Electroencephalography (EEG) and Magnetoencephalography (MEG) are widely used non-invasive techniques in clinical and cognitive neuroscience. However, low spatial resolution measurements, partial brain coverage by some sensor arrays, as well as noisy sensors could result in distorted sensor topographies resulting in inaccurate reconstructions of underlying brain dynamics. Solving these problems has been a challenging task, This paper proposes a robust framework based on electromagnetic source imaging for interpolation of unknown or poor quality EEG/MEG measurements.
Nanogold is an emerging material for enhancing surface-enhanced Raman scattering (SERS), which enables the detection of hazardous analytes at trace levels. This study presents a simple, single-step plasma synthesis method to control the size and yield of Au nanoparticles by using plasma-liquid redox chemistry. The pin-based argon plasma reduces the Au precursor in under 5 min, synthesizing Au spherical particles ranging from ∼20 nm at 0.
