https://www.mdu.se/

Ultrasound coupling technology is pivotal to ensuring high-quality diagnostic imaging, yet conventional water-based gels face persistent challenges, including acoustic impedance mismatch, air-bubble formation, dehydration, messiness, and cross-contamination risks. This review presents a comprehensive analysis of the evolution, materials science, and clinical performance of ultrasound gel pads, an advanced alternative engineered for superior acoustic transmission, hygiene, and patient comfort. Historical progression from early coupling agents to modern polymeric and hydrogel-based pads is traced, highlighting breakthroughs such as bilayer hydrogels, nanocomposite reinforcements, metamaterial-inspired designs, and patient-specific 3D-printed pads. Comparative evaluations demonstrate that gel pads, particularly those integrating nanotechnology, rival but often outperform traditional gels in transmission efficiency, near-field resolution, and adaptability to complex anatomical surfaces, while offering reusability and reduced environmental impact. For instance, solid gel pads achieved 92.3% stone disintegration, compared with 45.5% for semi-liquid gel, in ESWL phantom studies (p < 0.001). Materials, including polyacrylamide, silicone, and advanced hydrogels, are analyzed for mechanical properties, biocompatibility, and sustainability, with emphasis on biodegradable and locally sourced alternatives. Manufacturing innovations ranging from continuous casting to additive manufacturing enable customization, functional integration, and scalable production, although cost, supply chain stability, and regulatory compliance remain critical barriers. By uniting advances in materials engineering, nanotechnology, and precision manufacturing, ultrasound gel pads have demonstrated strong potential to advance coupling media for diagnostic, therapeutic, and wearable ultrasound applications, enabling higher diagnostic accuracy, streamlined workflows, and patient-centered care across diverse clinical and resource-limited settings.

Cardiovascular diseases (CVDs) are a significant and widespread cause of death in the world, continuing to increase mortality rates. Therefore, timely identification and diagnosis are essential for a patient's optimized recovery and longevity. In this regard, ECG is an effective tool for detecting anomalous heart conditions. However, interfering factors like noise, transient changes, and missing data could affect the accurate diagnosis of CVDs. The Cardiovascular ECG Data Recovery and Classification Network (CEDRC-Net) sorts and collates noisy data while also recovering missing data caused by equipment malfunction or human error, using a multistage machine-learning and deep-learning model. Moreover, CEDRC-Net is incorporated into the Transformer-based Convolutional Denoising Autoencoder (TCDAE) model to methodically mitigate noise, subsequently the Variational Autoencoder (VAE) or Temporal Fusion Transformer (TFT) are systematically employed as an alternative to accurately reconstruct and forecast ECG signals. Following this, the system classified heart diseases, including atrial fibrillation, sinus bradycardia, and tachycardia, using several machine-learning algorithms, based on data from a dataset comprising 2426 patients.TFT showcased better performance than VAE in ECG signal reconstruction, achieving up to 98% accuracy with Gradient Boosting, compared to 96% by VAE. Furthermore, in downstream classification, signals enhanced by TFT led to superior model results, with SVM and XGBoost both reaching 98.4% accuracy and F1-scores. The TFT achieved substantially lower reconstruction errors (MAE = 0.015, MSE = 0.00045, RMSE = 0.0132) compared to the VAE (MAE = 0.075, MSE = 0.011, RMSE = 0.107). These results highlight TFT's strong denoising capability for improving ECG diagnostic accuracy, while the suggested system ensures reliable measurements under noisy and non-ideal conditions. It is highly conducive for early and accurate diagnosis, better clinical decisions, and decreased patient load on the cardiologists. The research is performed on the MIMIC-IV-ECG 12-lead real-time dataset.

Cardiovascular diseases (CVDs) remain the leading cause of global mortality, driving the demand for innovative, non-invasive, and continuous monitoring technologies. Electrospun nanofibers have emerged as a versatile solution due to their high surface area, tunable porosity, mechanical flexibility, and biocompatibility. These properties enable their integration into wearable sensors, biosensors, and regenerative scaffolds for real-time cardiovascular health and therapy monitoring. This review comprehensively explores the evolution of electrospinning techniques, including coaxial, centrifugal, multi-coaxial, side-by-side, and multilayer methods, and their applications in developing advanced cardiovascular devices. Emphasis is placed on electrospun nanofibers for pulse wave and blood pressure monitoring, ECG acquisition, cardiac biomarker detection, and tissue engineering. Additionally, the review discusses polymer selection, fabrication parameters, and the challenges of clinical translation. By highlighting recent innovations and future directions, this work underscores the transformative potential of electrospun nanofibers in personalised cardiovascular diagnostics and regenerative medicine.

Electrical bioimpedance (EBI) is widely used for body composition analysis and shows promise for assessing muscle activation during physical activities (PAs), particularly in aging. This study investigated EBI’s sensitivity to age-related changes in muscle function by analyzing data from 40 adult participants divided into young (20–29 years), middle-aged (32–60 years), and older (62–73 years) groups. EBI signals were recorded from the Quadriceps and Extensor Digitorum Longus (EDL) muscles during three PAs: relaxed standing position, squats, and lunges. Key features were extracted to identify age-related differences. Results revealed distinct muscle-specific patterns: In the relaxed standing position, the EDL muscle exhibited a consistent, monotonic decline in the PrePAmagnitude feature from young to old adults, while the Quadriceps muscle displayed greater variability and a non-monotonic trend. Among the dynamic activities, squats revealed the most pronounced age-related differences, with 62.5% of the features showing statistical significance, whereas fewer differences in the features (25%) where shown during lunges. The findings suggest that EBI can detect age-related reductions in muscle activation and neuromuscular coordination, supporting its potential as a non-invasive tool for functional muscle assessment in aging.

High-sensitivity tunnel magneto resistance (TMR) sensors offer promising capabilities for non-invasive biomedical applications including magnetocardiography and magnetoencephalography. This study investigates a practical micro controller-based implementation using a TMR2905 sensor to simultaneously measure DC and AC magnetic fields. For DC and low-frequency measurements below 5 Hz, a timing-based direct capacitor interface was implemented, for higher AC frequencies, analog signal conditioning and filtering enables direct ADC conversion. Experimental validation against calibrated reference magnetometers demonstrates 99% accuracy for both DC and low-frequency AC measurements up to 8 kHz across all three orthogonal axes. The AC field response exhibits the expected logarithmic decrease with distance from the field source, with reliable detection maintained out to 150 mm separation. These results confirm the viability of integrating TMR sensors with standard microcontrollers for portable, multi-axis biomedical magnetic field monitoring without requiring cryogenic cooling.

Cardiovascular diseases (CVDs) remain the leading causes of global morbidity and mortality, underscoring the urgent need for advanced, non-invasive monitoring technologies. This review explores the integration of hybrid magnetic and optical sensors in wearable devices aimed at transforming cardiovascular health monitoring. Magnetic sensors, known for their strong signal capture and resistance to ambient light interference, complement optical sensors like photoplethysmography (PPG), which provide vital hemodynamic data, including blood volume changes and pulse rates. Combining these technologies improves monitoring systems' overall signal integrity, accuracy, and reliability. We critically evaluate current integration strategies, highlighting advanced multi-sensor data fusion techniques that enhance temporal resolution and enrich physiological data interpretation. Additionally, this review addresses the challenges in developing hybrid sensor systems, such as calibration accuracy, real-time data analytics, power optimization, and user-friendly design. Looking forward, we propose research directions focused on refining sensor integration frameworks to enable early detection and personalized management of CVDs. The synergy between magnetic and optical sensors significantly advances next-generation wearable diagnostics, greatly improving patient care and health outcomes. This review provides a comprehensive overview of the transformative potential of hybrid sensor technology in cardiovascular monitoring. 

Acute Lymphoblastic Leukemia (ALL), a cancer affecting the blood and bone marrow, requires precise classification for accurate diagnosis, personalized treatment plans, and improved predictive assessments to enhance patient survival and quality of life. This study presents LEU3, a novel classification model designed to improve the accuracy of leukemia detection from peripheral blood smear (PBS) images. LEU3 leverages an attention-based convolutional neural network (CNN) architecture, incorporating pooling layers, a global average pooling layer, and dense layers with dropout for regularization. The model is trained with an Adam optimizer comprising with four classes: Benign, early malignant pre-B, malignant pre-B, and malignant pro-B. Data augmentation techniques were employed to increase training set diversity. Additionally, Shapley Additive Explanations (SHAP) and Local Interpretable Model-agnostic Explanations (LIME) are used to enhance interpretability and transparency in the model's decision-making process. LEU3 achieved a test accuracy of 99% and a validation accuracy of 99% on 484 PBS images, demonstrating a 3% improvement over the baseline model. These results underline the potential of LEU3 in supporting medical professionals by reducing diagnostic workload and improving the accuracy of leukemia classification.

Hypertension is a global public health concern affecting over 1.13 billion individuals worldwide, contributing significantly to cardiovascular morbidity and mortality. This study aims to leverage machine learning techniques for hypertension classification based on Photoplethysmography (PPG) data, providing a comprehensive tutorial for researchers and healthcare professionals.The purpose of this study is to utilize machine learning models to analyze the publicly availablePPG-BP dataset, enabling the classification of hypertension categories without requiring extensiveprogramming expertise. The methodology involves using ChatGPT to generate MATLABcompatiblecode based on different queries, facilitating data preprocessing, feature selection, and the implementation of classification models, including Random Forest, Support Vector Machine (SVM),and Neural Networks. The models were evaluated based on mean absolute error, accuracy, and Rsquared(R²) values. The study includes 219 subjects with various attributes such as age, sex, height, weight, systolic anddiastolic blood pressure, heart rate, and BMI. The dataset categorizes individuals into differenthypertension stages, including normal, prehypertension, stage 1 hypertension, and stage 2 hypertension. The main results showed that the Random Forest model outperformed other models, achieving the highest classification accuracy and prediction performance. The most important predictors identified were SystolicBP and DiastolicBP. Precision and recall metrics indicated robust model performance,with Random Forest achieving the highest accuracy and the most precise feature importance ranking. In contrast, the Neural Network model exhibited comparatively lower prediction accuracy andhigher mean absolute error.This study demonstrates the potential of machine learning techniques in empowering health careprofessionals to conduct complex data analyses for hypertension prediction. The findings highlight the effectiveness of using ChatGPT to automate the generation of MATLAB-compatible code,making it a valuable tool for healthcare professionals with limited programming skills.

Photoplethysmography (PPG) signals offer a non-invasive and cost-effective mean sfor monitoring cardiovascular health. However, extracting clinically relevant information from these signals in real-time poses significant challenges. This study aims to develop a portable cardiovascular disease (CVD) monitoring system using embedded systems for real-time PPG signal processing and feature extraction. The purpose of this study is to utilize embedded systems, specifically ESP32 boards, to play andprocess PPG waveforms from the PPG-BP dataset. The PPG waveform is saved into the controllerand played using the onboard DAC, while another embedded system receives this waveform using ADC and applies real-time filtering. The filter configuration used is based on the Chebyshev type II4th order, 20 dB filter with a frequency range of 0.4–8 Hz. To implement the filter into thee mbedded system, filter coefficients were calculated and C++ code was written for the filter. Real-time filtering presented several challenges, including managing computational load, ensuring signal integrity, and minimizing latency. Once the filtered waveform is achieved, the next steps involve calculating features as obtained in the PPGFeat toolbox. These features include pulse transit time, augmentation index, stiffness index, various magnitudes, and time intervals, which characterize the PPG signal's morphology and timing intervals. The main results demonstrate the system's ability to perform real-time signal processing and feature extraction, providing continuous monitoring and analysis of cardiovascular health. The future prospect is to test the system on different embedded systems to monitor real-time performance for complex actions, aiming to enhance the portability and efficiency of cardiovascular disease monitoring. In conclusion, this study presents a novel approach to developing a portable CVD monitoring system using embedded systems for real-time PPG signal processing. The findings highlight the potential of embedded systems in providing efficient and accurate cardiovascular health monitoring, paving th eway for personalized healthcare solutions.