Treatment of Nocturnal Enuresis Using Miniaturised Smart Mechatronics With Artificial Intelligence.

Our study was designed to develop a customisable, wearable, and comfortable medical device - the text so-called "MyPAD" that 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 text pre-void stage using miniaturised mechatronics with Artificial Intelligence (AI).

Pupil Localisation and Eye Centre Estimation Using Machine Learning and Computer Vision.

Various methods have been used to estimate the pupil location within an image or a real-time video frame in many fields. However, these methods lack the performance specifically in low-resolution images and varying background conditions. We propose a coarse-to-fine pupil localisation method using a composite of machine learning and image processing algorithms.

Intelligent autonomous treatment of bedwetting using non-invasive wearable advanced mechatronics systems and MEMS sensors : Intelligent autonomous bladder monitoring to treat NE.

Post-void alarm systems to monitor bedwetting in nocturnal enuresis (NE) have been deemed unsatisfactory. The aim of this study is to develop a safe, comfortable and non-invasive pre-void wearable alarm and associated technologies using advanced mechatronics. Each stage of development includes patient and public involvement and engagement (PPI).

Patient Engagement in Medical Device Design: Refining the Essential Attributes of a Wearable, Pre-Void, Ultrasound Alarm for Nocturnal Enuresis.

Background: To date, no pre-void wearable alarm exists to treat nocturnal enuresis (NE)-night-time bedwetting, and children with NE and their families are disappointed in relation to the post-void moisture alarms and medicine currently available. Development of a safe, comfortable and non-invasive wearable pre-void alarm and associated technology, using advanced mechatronics, is underway (the MyPAD device). Each stage of development includes patient and public involvement (PPI), particularly with respect to human factors, in collaboration with physicians, radiologists, psychologists, nurses, engineers and designers.

Feasibility study of intelligent autonomous determination of the bladder voiding need to treat bedwetting using ultrasound and smartphone ML techniques : Intelligent autonomous treatment of bedwetting.

Unsatisfactory cure rates for the treatment of nocturnal enuresis (NE), i.e. bed-wetting, have led to the need to explore alternative modalities.

Biomedical visual data analysis to build an intelligent diagnostic decision support system in medical genetics.

Background: In general, medical geneticists aim to pre-diagnose underlying syndromes based on facial features before performing cytological or molecular analyses where a genotype-phenotype interrelation is possible. However, determining correct genotype-phenotype interrelationships among many syndromes is tedious and labor-intensive, especially for extremely rare syndromes. Thus, a computer-aided system for pre-diagnosis can facilitate effective and efficient decision support, particularly when few similar cases are available, or in remote rural districts where diagnostic knowledge of syndromes is not readily available.

Optimization and enhancement of H&E stained microscopical images by applying bilinear interpolation method on lab color mode.

Background: Hematoxylin & Eosin (H&E) is a widely employed technique in pathology and histology to distinguish nuclei and cytoplasm in tissues by staining them in different colors. This procedure helps to ease the diagnosis by enhancing contrast through digital microscopes. However, microscopic digital images obtained from this technique usually suffer from uneven lighting, i.