10050419Intelligent DEPression Tracking (I-DEPT) - A Novel, Longitudinal and Multi-Modal Machine Learning Framework for Quantifying Depression SymptomsClosedCollaborative R&DInnovate UKDepression is a massive, growing economic and societal problem; it is a leading cause of disability and suicides, costing the UK economy \>£56bn annually in lost productivity. There is an urgent need to relieve this burden on the economy and the NHS. The healthcare sector, however, needs better tools to tackle the problem of depression efficiently. Its heterogeneous clinical profile means that patients can have several unique combinations of depressive symptoms. Currently, identifying the right diagnosis and treatment in the UK may take many years, with some studies finding rates of untreated depression as high as 77%. There is an urgent need for a tool that can help clinicians objectively measure individual depression _symptoms and symptom clusters_ - just as physical illness ones (e.g. blood test markers). Artificial intelligence (AI) tools have been proposed to automatically detect depression (but not symptoms like mood, fatigue or anhedonia). They also face barriers to commercial implementation given their limited validity and generalisability. This is due to their focusing on small samples, a single time point and/or a single behaviour modality (e.g. voice, which reduces accuracy and sensitivity). **The aim of this project is to bridge this gap by developing an innovative healthcare solution in the form of a next generation, reliable AI-powered depression screening tool for clinicians ("I-DEPT").** To this end, we will collect several minutes' worth of gamified activities twice a week, over 3 months, from 550 depressed individuals and 550 controls via an online recruitment platform (Prolific; e.g. speech elicitation, working memory etc.). Ultimately, our end-to-end clinician support tool will save clinicians significant admin time and costs, doubling patient throughput and revenue, halving waiting times, all whilst improving clinical outcomes. The project will take us from proof-of-concept to a fully commercialisable product with strong, demonstrable return on investment.