Search

Objective: Our study was designed to develop a customisable, wearable medical — so-called — MyPAD device that generates pre-void alarms using miniaturised mechatronics with Artificial Intelligence (AI). Methods and procedures: The developed features include: multiple bespoke ultrasound (US) probes for sensing, a bespoke electronic device housing custom US electronics for signal processing, a bedside alarm box for processing the echoed pulses and generating alarms, and a phantom to mimic the human body. The validation of the system is conducted on the tissue-mimicking phantom and volunteers using Bidirectional Long Short-Term Memory Recurrent Neural Networks (Bi-LSTM-RNN) and Reinforcement Learning (RL). Results: A Se value of 99% and a Sp value of 99.5% with an overall accuracy rate of 99.3% are observed. Conclusion: The obtained results demonstrate successful empirical evidence for the viability of the device, both in monitoring bladder expansion to determine voiding need and in reinforcing the continuous learning and customisation of the device for bladder control through consecutive uses.

This research was designed to evaluate if it is viable to awaken children with urinary incontinence at the pre-void phase using a smart wearable device and enable them to control incontinence with fine-tuned individual parameters determined by the device intelligently. To address this research question, a miniaturised wearable smart device was built in this multidisciplinary research to monitor the non-linear behaviours of the bladder during its expansion with urine intake. The device, with its customisable abilities, sets an individual alarm point to awaken the child with incontinence before voiding. Safety parameters, aesthetics and ergonomic use of the device were investigated through hospital trials with children and the device was improved based on the obtained feedback from these trials.Clinical Relevance: The device will help children learn how to control their incontinence over time.

A smart, wearable, medical device -- the so-called ``MyPAD'' which monitors the fullness of the bladder, triggering an alarm indicating the need to void, in order to prevent badwetting -- i.e., treating Nocturnal Enuresis (NE) at the pre-void stage, was developed in this research. Patient and public engagement was an integral part of each development phase, particularly with respect to human factors, in collaboration with physicians, radiologists, psychologists, nurses, engineers and designers. Feedback was obtained from real-world trials and further improvements were incorporated into the device. Clinical Relevance: MyPAD will treat the NE efficiently against wetness-based post-void alarms and will enable children to gain control of incontinence over time.

Objective: Our study was designed to develop a customisable, wearable medical — so-called — MyPAD device that generates pre-void alarms using miniaturised mechatronics with Artificial Intelligence (AI). Methods and procedures: The developed features include: multiple bespoke ultrasound (US) probes for sensing, a bespoke electronic device housing custom US electronics for signal processing, a bedside alarm box for processing the echoed pulses and generating alarms, and a phantom to mimic the human body. The validation of the system is conducted on the tissue-mimicking phantom and volunteers using Bidirectional Long Short-Term Memory Recurrent Neural Networks (Bi-LSTM-RNN) and Reinforcement Learning (RL). Results: A Se value of 99% and a Sp value of 99.5% with an overall accuracy rate of 99.3% are observed. Conclusion: The obtained results demonstrate successful empirical evidence for the viability of the device, both in monitoring bladder expansion to determine voiding need and in reinforcing the continuous learning and customisation of the device for bladder control through consecutive uses.

Over the past few decades, large volumes of healthcare data in different formats with multiple dimensions and complexities (i.e., the 10 Vs of Big Data), beyond the comprehension of human capacity have been created exponentially. New intelligent tools and methods are required to exploit these data volumes, particularly obtained using advanced sensor technologies (e.g., biosensors, medical imaging, laboratory tests, and genetic tests). Data analytic tools - Artificial Intelligence & Machine Learning (AI/ML) techniques - in the healthcare discipline are evolving rapidly to harvest these explosive increasing healthcare data volumes by uncovering complex nonlinear relationships and patterns given the particular datasets leading to thorough and up-to-date insights in a timely manner for early diagnosis of diseases, better treatment, effective decision-making, reducing healthcare costs, and making our lives safer, better and simpler in every aspect. Within this context, the objective of this themed article collection aims to cover the following subjects particularly focusing on the pediatrics discipline. • Big data analytics in pediatrics, • Disease databases in pediatrics (e.g., rare diseases, disorders), • Intelligent applications using the pediatrics disease databases, • Intelligent methods processing every type of pediatric data, • Datamining in pediatrics, • Precision medicine/Personalized medicine in pediatrics using AI/ML, • Diagnostics in pediatrics using AI/ML, • Prognosis in pediatrics using AI/ML, • Deep learning (DL) and Reinforcement Learning (RL) in pediatrics, • Development of new treatment methods in pediatrics using AI/ML, • Reduction of medical errors in pediatrics using AI/ML, • Intelligent machines in pediatrics, • Telemedicine in pediatrics using AI/ML, • Discovery of new drugs in pediatrics using AI/ML, • Use of cloud computing to process pediatric data, • Ethics and regulatory framework of using AI/ML in pediatrics, • Review papers discussing the us

Previous research suggests that transgender men and women are more likely to experience body dissatisfaction and disordered eating. Few studies have, however, investigated the manner in which body dissatisfaction and eating behavior are affected by the gender affirmation process. To address this issue, semi-structured interviews were conducted with transgender men and women (N = 22) recruited from British support groups. Participants were aged 19–71 years. Participant sexuality included heterosexual, homosexual, pansexual, and asexual orientations and all participants identified themselves as white. For both transgender men and women, analyses revealed a shift from a focus on psychological wellbeing in the early stages of gender affirmation to physical wellbeing in the later stages. While body dissatisfaction appeared to dissipate as gender affirmation progressed, a common theme across the gender affirmation process was that both transgender men and women engaged in risky behaviors related to transforming body shape and size. Findings highlight the need to consider the influence of gender affirmation when researching the interconnections between attitudes, behavior, and emotions relating to gender identity.

Objective: Family dysfunction and self-esteem play an important role in the development of eating disorders (EDs), but this role has not been sufficiently examined regarding eating pathology and psychosocial quality of life (QoL), which often remains unchanged even after ED symptoms reduce. The purpose of this study was to therefore assess the mediating role of self-esteem between family dysfunction and both eating pathology and psychosocial QoL in ED patients and controls. Method: One hundred and fifty four female adult ED patients and 154 female healthy adult controls were recruited from Athens, Greece, and self-reported measures were used to assess family dysfunction, eating pathology, self-esteem, and psychosocial QoL. Structural equation modelling (SEM) was employed to test the mediation hypotheses. Results: For both the ED and control groups, family dysfunction levels did impact eating pathology, but only through self-esteem. Family dysfunction, self-esteem, and eating pathology had a direct effect on both groups' psychosocial QoL. Conclusion: Self-esteem's important role in EDs was confirmed in both groups, along with its sensitivity to family dysfunction. We propose a parsimonious yet comprehensive theoretical model of the role of family dysfunction and self-esteem in EDs which future studies should further investigate longitudinally and in other population groups.

Post-void alarm systems to monitor bed wetting in Nocturnal Enuresis (NE) have been deemed unsatisfactory. The aim in this study is to develop a safe, comfortable and non-invasive pre-void wearable alarm and associated technology using advanced mechatronics. Each stage of development includes patient and public involvement and engagement (PPI). The early stage of the development involved children with and without nocturnal enuresis (NE) (and parents) who were tested at a hospital under the supervision of physicians, radiologists, psychologists, and nurses. The readings were simultaneously compared to B-mode images and measurements, acquired from a conventional ultrasound device, and were found to correlate highly. The results showed that determining imminent voiding need is viable using non-invasive sensors. Following on from "proof of concept", a bespoke advanced mechatronics device has been created. The device houses custom electronics, an ultrasound system, intelligent software, a user-friendly smartphone application, bedside alarm box and a dedicated undergarment, along with a self-adhesive gel pad - designed to keep the MEMS sensors aligned with the abdomen. Testing of the device with phantoms and volunteers has been successful in determining bladder volume and associated voiding need. Five miniaturized, and therefore more ergonomic, versions of the device are being developed, with an enabled connection to the cloud platform for location independent control and monitoring. Thereafter, the enhanced device will be tested with children with NE at their homes for 14 weeks, to gain feedback relating to wearability and data collection involving the cloud platform.