New digital assessment for reviewing skin care needs
The University of Bradford’s Centre for Skin Sciences worked with Beauty Consocaire Ltd and Skin Thinking Ltd to understand whether a new digital skincare diagnostic tool, developed by Beauty Consocaire, works as well as a professional skin care grader in identifying skincare needs.
The digital tool was web-based and modelled likely skincare needs for a diverse range of skin types and was particularly directed to include all ethnicities.
To do this validation, the Centre for Skin Sciences teamed up with us, the Digital Health Enterprise Zone, to use our clinical rooms where the participants, after a gentle facial cleansing, were examined by a professional skin care grader whose score was then compared with the scores from the diagnostic tool completed by the participant. Dr Gill Westgate, Business Development Manager said “The DHEZ clinical rooms and reception area were ideal for this type of study.”
Dr Liz Breen, Director of the Digital Health Enterprise Zone said “This study clearly demonstrates the increased adoption of technology in skin science healthcare research and treatments and is an excellent example of the research that we engage in within DHEZ”.
The Centre for Skin Sciences designed the study to compare an expert’s assessment and the tools assessment against a range of skin concerns. Volunteers completed an online questionnaire at home, the digital skincare diagnostic tool, then attended DHEZ to see a professional skin care grader.
The study informed important updates to the algorithms of the digital tool, highlighted the potential reasons for differences in results, but very importantly showed that the online tool provides the basis for good advice on users’ skincare concerns.
The results of the study were published in the Cosmetics and Toiletries magazine.
Beauty Consocaire launched the App and a new product range in 2020 https://wearewo.com/
Final Year Media Design & Technology Student Project
The Director of DHEZ spent spent semester two working with a group of final year students from the School of Media, Design & Technology (Faculty of Engineering & Informatics, University of Bradford) on a project connected to their study to create an animation that explains digital pathology.
Assessing skincare needs for diverse ethnicities
Type of Project: Evaluation project requiring local ethics approval
Project Lead: Dr Gillian Westgate, Centre for Skin Sciences, University of Bradford
Partners: Beauty Consociare Ltd
Co-investigator: Dr Julie Thornton, Centre for Skin Sciences, University of Bradford
Expert Skincare Grader: Dr Katerina Steventon , Independent Skincare Consultant
Project Summary: Beauty Consocaire Ltd has developed an algorithm-based web tool to predict an individual’s top 3 skincare needs for launch as part of a new all inclusive skin care brand - Wo. The company approached the University to design a validation study in which the results of the digital diagnostic is compared with an in person skincare assessment from an expert skin care consultant, using the facilities at the DHEZ. Study participants were selected to attend face to face examinations at the DHEZ after completing their online surveys. Respondents were female volunteers, aged between 18 and 35 with diverse ethnic backgrounds. The web tool proved to be accurate to predict skincare concerns and needs and all participants found it valuable to take part.
Development of Digital Health Platforms for processing patient data using Artificial Intelligence and visual computing Technologies
Type of Project: R & D Ph.D. research project (AI & Visual computing)
Project Lead: This research project started in September 2019. The project is led by Prof. Rami Qahwaji and Dr. Ci Lei, in collaboration with the NHS (Dr Tom Lawton, Head of Clinical Artificial Intelligence at Bradford Teaching Hospitals).
Project Summary: There have been considerable advances in AI, visual computing, Internet of Things (IoT) and wearable sensors in recent years, and the arrival of the 5G wireless spectrum will provide the capacity for new sensor platforms and devices to capture and share data reliably between devices and healthcare providers. Relying more on technology and automated systems has become the norm, the medical and health industry is no exception. To help with the prediction of patient mortality and stability within the ICU (intensive care unit), clinical support systems are utilized. Having access to patient data can help with the correct distribution of resources. One major element that needs to be considered is that in clinical settings, systems that are being used are limited in terms of their predictive value. This project aims to develop a new digital healthcare system, which would be designed to exploit recent advances in computing and communication technologies and will build on its collaboration with the NHS, to deliver a new digital health platform for processing patients’ data using advanced medical imaging and machine learning techniques.
Project Aims: We are looking into developing an algorithm that can help predict patient mortality in the ICU with the help of artificial intelligence and visualisation. We plan to achieve this by using features that have a direct relationship with patient mortality as well as creating a separate system to help distinguish trends in data. For this project, live patient data from Bradford Royal Infirmary will be used. Visualising live data provides benefits that can activate faster decision makings, incorporate emerging trends, recognize connections between results & operations as well as the ability to interact with the data.
For further information please contact:
Researcher: Nima Donyanavard, e-mail: email@example.com
Interprofessional Practice – Dementia Scenario
Project Lead – Dr Jae Hargan and Service User and Carer Involvement Group
Project Partners – University of Bradford staff: Dr Melissa Owens, Helen Cook, Cathy Clarke, Hilary Pape, Jamie Beck, Jean Gallagher, Lindsay Hobbs, Clare Mason, Vicky Milburn
Project Summary: To enable health and social care students to identify how their individual professional roles and interprofessional partnership working can influence the quality of care and outcomes for a person with dementia and their carer in a crisis.
Computer Vision for Diagnosis of Parkinson’s Disease
Type of Project: Research and Development
Project Lead: Prof Rami Qahwaji
Project Summary: This RDF-funded Project is led by Prof Rami Qahwaji (PI) and includes Prof Raed Abd-Alhameed, Dr John Buckley, Dr Amr Rashad Abdullatif and Eng Ramzi Jaber (Funded PhD Studentship).
Parkinson’s disease is the second most common neurodegenerative disease, with a lifetime risk of around 2% [Ascherio and Schwarzschild, 2016], and causes muscle rigidity, tremors, and changes in movement and gait. There are 127,000 people diagnosed with Parkinson’s in the UK, the prevalence rising with an ageing population. Neurologists depend on observation of the patients to make clinical decisions about diagnosis, disease monitoring and treatment [Clarke et al., 2016]. However, visual observation is highly subjective and depends mainly on the clinician’s experience. Moreover, small or subtle changes in movement patterns and gait, which could be early signs of Parkinson’s disease, are usually missed by clinicians.
Bradykinesia is one of the cardinal symptoms associated with Parkinson’s disease. Typically characterised as a movement disorder, bradykinesia can be represented according to the degree of motor impairment. The inability to initiate movement effectively can stem from a combination of slowness of voluntary and involuntary or delayed movement. Parkinson’s disease (PD) can further cause tremors and stiffness which must also be considered in clinically assessing the degree of motor impairment. The assessment criteria for PD is therefore well defined due to the symptomatic nature associated with Parkinson’s. In the early stages of Parkinson’s irregularities in movement may be mild.