Title: An Artificial Intelligence enhanced, Mobile based Technology to Empower patients in Collecting Visible Biomarkers –AIMTEC VB
Funder: Versus Arthritis

Abstract: Recent progress in the field of Artificial Intelligence (AI) is boosting the feasibility of personalised care, carrying both the promise of improved patients’ outcomes and the burden of increased volume of data to be acquired and analysed. Automated systems to “triage” patient care reducing healthcare costs are proving popular and successful (e.g. Babylon Health improving patient access to healthcare, and increasing efficiency for clinicians and administrators). Our “Artificial Intelligence enhanced, Mobile based Technology to Empower patients in Collecting Visible Biomarkers” project aims to empower patients affected or at risk of developing Scleroderma (SSc) or Psoriatic Arthritis (PsA) by collecting images corresponding to the site of their symptoms. Low-cost photographic recordings from ubiquitous devices will validate the proof of concept of a mobile phone-based technology. Following an extreme phenotype approach, images collected from the most severe and the most benign groups of patients with Raynaud’s and Psoriasis, will be used to train an AI algorithm and identify key signals and differences in discrete aspects of the imagery. The software will then be released for beta testing adoption by patient charities and included in data collection infrastructures in the two conditions. The project will build on the collaboration between Leeds NIHR Biomedical Research Centre (BRC) and Arthronica ltd. The Leeds BRC is currently funded to implement stratified medicine approaches based on patient phenotyping in both SSc and PsA. The existing infrastructure will secure patient access and engagement and will in turn, benefit for the AI data set in the stratified medicine modelling. Arthronica ltd will provide expertise and resources for optimization and training of the AI algorithms used in this study. Main objectives: to determine the proof of concept of a mobile phone-based technology for preliminary self-assessment and inform a route to widely deployable dissemination to empower patients in taking active role in “triage” screening for severe disease and reduce healthcare cost burden; to devise a mobile phone-based tool that will enrich the deep patient phenotyping dataset of the NIHR BRC, currently lacking in patient reported images, and aid stratified intervention approaches. AIMTEC VB project vision aligns with VA mission through: The short-term vision: to establish proof of concept feasibility of a tool to enhance the engagement of patients in establishing personalised medicine approaches without increasing the burden to the NHS. The long-term vision: to improve our understanding and ultimately the clinical outcome of autoimmune diseases through personalised and prevention approaches. For this purpose, we propose to address the following key technical challenges: create of a low-computational visual mobile-based technology to process images uploaded from patients; build a visual tracker that is robust enough to work in the presence of different skin tones, rings, tattoos, and skin lesions; identify a predictive statistical model of disease severity based on deep neural networks models that will directly learn from the data uploaded from patients.


Research activity codes: 4.2 EVALUATION OF MARKERS AND TECHNOLOGIES
Health categories: INFLAMMATORY AND IMMUNE SYSTEM