Advances in breast cancer diagnosis: a comprehensive review of imaging, biosensors, and emerging wearable technologies.

Breast cancer has been the most frequent diagnosed cancer and the leading cause of cancer-related deaths among women worldwide, mainly due to delayed detection. Early diagnosis significantly improves prognosis and long-term survival rates. Various techniques, including imaging, sensors, and molecular biotechnology, have been developed to facilitate early detection.

Analysing the Association Between User Perceptions and Usage Patterns of Digital Personalised Care Platforms Using the UTAUT Framework: Insights from the ADLIFE Project.

This study explores the relationship between user perceptions, as measured by the extended Unified Theory of Acceptance and Use of Technology (UTAUT) model, and actual usage patterns of the ADLIFE Digital Personalized Care Platform. Data were collected from healthcare professionals and patients/informal caregivers across multiple pilot sites. Significant correlations were found between usability factors, technology anxiety, performance expectancy, and platform usage metrics, underscoring the importance of user-centred design and support systems.

A Novel and Secure 3D Colour Medical Image Encryption Technique Using 3D Hyperchaotic Map, S-box and Discrete Wavelet Transform.

Over the past two decades, there has been a substantial increase in the use of the Internet of Medical Things (IoMT). In the smart healthcare setting, patients' data can be quickly collected, stored and processed through insecure medium such as the internet or cloud computing. To address this issue, researchers have developed a range of encryption algorithms to protect medical image data, however these remain vulnerable to brute force and differential cryptanalysis attacks by eavesdroppers.

Recent Advances in Generative Models for Synthetic Brain MRI Image Generation.

With the use of artificial intelligence (AI) for image analysis of Magnetic Resonance Imaging (MRI), the lack of training data has become an issue. Realistic synthetic MRI images can serve as a solution and generative models have been proposed. This study investigates the most recent advances on synthetic brain MRI image generation with AI-based generative models.

Transforming Healthcare: The Role of Artificial Intelligence.

The integration of artificial intelligence (AI) into healthcare is revolutionising the industry by enhancing diagnostic accuracy, personalising treatment strategies, and improving administrative efficiency. This study aims to evaluate the impact of AI interventions on health outcomes across various medical applications. A scoping review was conducted using relevant search terms, focusing exclusively on interventional studies measuring AI's effectiveness on health outcomes.

Preliminary Exploration of Pre-Trained Vision-Language Models for Cyber-Threat Modelling in Internet of Medical Things (IoMT).

The field of image processing is undergoing a significant transformation, driven by the advancements in vision-language models (VLMs) based on groundbreaking transformer architectures. With the expansion of Internet of Medical Things (IoMT) devices, the need for robust and efficient threat detection methods has become increasingly critical. The rapid expansion of IoMT networks demands solutions that can automatically identify network-based threats with high precision and low computational cost.

Patients vs. Healthcare Providers: A Comparative Analysis of Technology Acceptance Using the UTAUT Model.

This study examines the differences in technology acceptance of electronic Patient Empowerment Platforms (PEP) and Personalised Care Plan Management Platforms (PCPMP) between two distinct user groups: patients/caregivers and healthcare providers, across three linguistic and cultural contexts (Danish, Hebrew and Russian). Using the Extended Unified Theory of Acceptance and Use of Technology (UTAUT) model and framework, we analysed responses from 92 participants. Our findings reveal differences in the perceived usability factors and facilitating conditions between patients and healthcare providers, suggesting the need for tailored interventions to improve PEP/PCPMP adoption among diverse user groups.

A novel transformer-based approach for cardiovascular disease detection.

According to the World Health Organization, cardiovascular diseases (CVDs) account for an estimated 17.9 million deaths annually. CVDs refer to disorders of the heart and blood vessels such as arrhythmia, atrial fibrillation, congestive heart failure, and normal sinus rhythm.

Understanding User Experiences on Patient Empowerment and Personalised Care Platforms: A Longitudinal Analysis Through the Extended UTAUT Model.

This study investigates the factors influencing the adoption and sustained use of the Patient Empowerment Platform (PEP) and Personalised Care Plan Management Platform (PCPMP) among patients and healthcare providers. Using the extended Unified Theory of Acceptance and Use of Technology (UTAUT) model, we analysed responses from 22 participants over a two-phase longitudinal study. Data were collected at two time points (at the beginning of the project and 4-6 weeks after) to assess changes in performance expectancy, effort expectancy, social influence, facilitating conditions, technology anxiety, adoption timeline, behavioural intention, and usability factors.

Preliminary Results on Improved Synthetic Image Generation for Melanoma Skin Cancer.

Advances in computer vision have shown interesting results in synthetic image generation. Diffusion models have shown promising outputs while generating realistic images from textual inputs like stable diffusion and Imagen. However, their use in high-quality medical images is limited.

EEG electrode setup optimization using feature extraction techniques for neonatal sleep state classification.

An optimal arrangement of electrodes during data collection is essential for gaining a deeper understanding of neonatal sleep and assessing cognitive health in order to reduce technical complexity and reduce skin irritation risks. Using electroencephalography (EEG) data, a long-short-term memory (LSTM) classifier categorizes neonatal sleep states. An 16,803 30-second segment was collected from 64 infants between 36 and 43 weeks of age at Fudan University Children's Hospital to train and test the proposed model.

INSAFEDARE Project: Innovative Applications of Assessment and Assurance of Data and Synthetic Data for Regulatory Decision Support.

Digital health solutions hold promise for enhancing healthcare delivery and patient outcomes, primarily driven by advancements such as machine learning, artificial intelligence, and data science, which enable the development of integrated care systems. Techniques for generating synthetic data from real datasets are highly advanced and continually evolving. This paper aims to present the INSAFEDARE project's ambition regarding medical devices' regulation and how real and synthetic data can be used to check if devices are safe and effective.

Deep Learning-Based Synthetic Skin Lesion Image Classification.

Advances in general-purpose computers have enabled the generation of high-quality synthetic medical images that human eyes cannot differ between real and AI-generated images. To analyse the efficacy of the generated medical images, this study proposed a modified VGG16-based algorithm to recognise AI-generated medical images. Initially, 10,000 synthetic medical skin lesion images were generated using a Generative Adversarial Network (GAN), providing a set of images for comparison to real images.

Development of a CNN for Adult Brain Tumour Characterisation: Implications and Future Directions for Transfer Learning.

Brain tumours are the most commonly occurring solid tumours in children, albeit with lower incidence rates compared to adults. However, their inherent heterogeneity, ethical considerations regarding paediatric patients, and difficulty in long-term follow-up make it challenging to gather large homogenous datasets for analysis. This study focuses on the development of a Convolutional Neural Network (CNN) for brain tumour characterisation using the adult BraTS 2020 dataset.

Automatic neonatal sleep stage classification: A comparative study.

Sleep is an essential feature of living beings. For neonates, it is vital for their mental and physical development. Sleep stage cycling is an important parameter to assess neonatal brain and physical development.

A convolutional neural network-based decision support system for neonatal quiet sleep detection.

Sleep plays an important role in neonatal brain and physical development, making its detection and characterization important for assessing early-stage development. In this study, we propose an automatic and computationally efficient algorithm to detect neonatal quiet sleep (QS) using a convolutional neural network (CNN). Our study used 38-hours of electroencephalography (EEG) recordings, collected from 19 neonates at Fudan Children's Hospital in Shanghai, China (Approval No.

Predicting Breast Cancer Leveraging Supervised Machine Learning Techniques.

Breast cancer is one of the leading causes of increasing deaths in women worldwide. The complex nature (microcalcification and masses) of breast cancer cells makes it quite difficult for radiologists to diagnose it properly. Subsequently, various computer-aided diagnosis (CAD) systems have previously been developed and are being used to aid radiologists in the diagnosis of cancer cells.

Predicting Feeding Conditions of Premature Infants Through Non-Nutritive Sucking Skills Using a Sensitized Pacifier.

Due to the lack of enough physical or suck central pattern generator (SCPG) development, premature infants require assistance in improving their sucking skills as one of the first coordinated muscular activities in infants. Hence, we need to quantitatively measure their sucking abilities for future studies on their sucking interventions. Here, we present a new device that can measure both intraoral pressure (IP) and expression pressure (EP) as ororhithmic behavior parameters of non-nutritive sucking skills in infants.

A Hybrid DCNN-SVM Model for Classifying Neonatal Sleep and Wake States Based on Facial Expressions in Video.

Sleep is a natural phenomenon controlled by the central nervous system. The sleep-wake pattern, which functions as an essential indicator of neurophysiological organization in the neonatal period, has profound meaning in the prediction of cognitive diseases and brain maturity. In recent years, unobtrusive sleep monitoring and automatic sleep staging have been intensively studied for adults, but much less for neonates.

Can pre-trained convolutional neural networks be directly used as a feature extractor for video-based neonatal sleep and wake classification?

Objective: In this paper, we propose to evaluate the use of pre-trained convolutional neural networks (CNNs) as a features extractor followed by the Principal Component Analysis (PCA) to find the best discriminant features to perform classification using support vector machine (SVM) algorithm for neonatal sleep and wake states using Fluke facial video frames. Using pre-trained CNNs as a feature extractor would hugely reduce the effort of collecting new neonatal data for training a neural network which could be computationally expensive. The features are extracted after fully connected layers (FCL's), where we compare several pre-trained CNNs, e.

Evaluation of Apgar Scores and Non-Nutritive Sucking Skills in Infants Using a Novel Sensitized Non-Nutritive Sucking System.

One of the challenges in examining development of newborns is measuring activities which are correlated to their health. Oral feeding is the most important factor in an infant's healthy development. Here, we present a new device that can measure intraoral and expression pressures produced in a newborn's mouth by non-nutritive sucking.