Title: Senti - Wearable technology to enable remote Precision and Predictive medicine for respiratory patients.
Funder: Department of Health and Social Care

Abstract: Background: Senti is a novel wearable medical device: a smart garment which enables remote and autonomous chest examination. Aims and Objectives: In parallel to seeking market approval for the Senti hardware, work will continue on developing machine learning algorithms to autonomously monitor chest sounds. Our vision is precision and predictive medicine for respiratory patients, delivered through the creation of predictors of deterioration, assistance in diagnosis (e.g. differentiating exacerbations of heart-failure from COPD), personalising treatments (through acoustic phenotyping of heterogeneous COPD/Asthma), enabling patients to make more informed self-care choices (such as when to take rescue packs) and optimisation care pathways (identifying appropriate services for ongoing care). Project plan and methods used: Our project runs in two streams. First; gaining market approval for our version-1 non-intelligent device. However; this grant will enable us to continue work in earnest on our autonomous system. A dataset of respiratory sounds will be captured using our device, via the clinical investigation of our version-1 device. A number of models will be trained to classify chest sounds and their underlying diagnoses. A retrospective cross-validation approach will be employed to validate these models. Timelines for delivery: This proposal will be delivered over nine months, starting in September. Anticipated Impact and Dissemination: Deploying the non-intelligent version of Senti (enabling remote chest examination) to community respiratory teams across the country will deliver immediate benefit to patients. However, the findings from our machine learning work will be published and disseminated. These two streams will further impact by positioning us to undertake a more robust validation of our autonomous device through a prospective trial; whilst also enabling us to gather further data from patients using our non-intelligent device. We will also be in a position then to complete a health economic analysis on the potential cost and benefit of our autonomous device.

Lay abstract: Our vision is to enable patients with chronic respiratory illnesses to enjoy more days without breathlessness, staying well at home. This ambitious goal can be achieved through democratising the stethoscope, the key tool in the assessment of respiratory patients. An artificial intelligent (AI) wearable array of stethoscopes would enable patients to benefit from the decision-making power of the clinical examination from the comfort and familiarity of their home. Creating an AI-enabled device requires significant volumes of data. To de-risk this development, we re developing an initial unintelligent device which enables remote chest examination through sensors embedded into a jacket. This unintelligent device adds value to community respiratory teams who will be able to remotely examine patients, saving 30% of nursing time (currently spent travelling). We have raised funds to develop this hardware already (now having a proof-of-concept prototype tested in the lab) and are working in partnership with the Mid and South Essex (MSE) hospital group (collaborators on this application) to bring this novel medical device to market, with patient participation from over 40 people with COPD. Within the current phase, we ll file a patent for Senti. This grant enables our next stage: preparation for applying AI to develop predictors of deterioration (in patients with long term respiratory conditions), assist in diagnosis, personalise treatments, enable patients to make more informed self-care choices (such as when to take rescue packs) and optimise care pathways. Funds will be used to acquire a large dataset of chest sounds via our device (through an 8-week, 100-patient clinical trial) which will also attain CE-marking of our hardware. The grant will enable us to hire a machine learning engineer to explore and prepare this dataset. We ll run feasibility studies to evaluate what autonomous functionality would be of greatest initial value and further develop prototypes of our initial algorithms. The outputs of this grant include obtaining market approval for our wearable remote chest examination device and lab-tested machine learning modules. Whilst revenue generated from this first device will support onward R&D, we will also be in a position to apply for phase 2 AI Grant funding or raise additional funds through private equity financing. A subsequent phase will enable us to CE-mark our AI-enabled product and prepare to scale-out across health services, including engaging with the NICE technology appraisal process. Through these stages, we ll continue to work with (and disseminate outputs to) our patient and clinicians groups, ensuring our products align with our users real problems and experiences. Seni will influence the entire respiratory patient journey through autonomous chest examination: enabling self-care through AI-guided advice, supporting acute assessment through detecting deterioration, monitoring response to treatment (in hospital and the community), and facilitating rapid, safe discharges.

Research activity codes: 4.1 DISCOVERY AND PRECLINICAL TESTING OF MARKERS AND TECHNOLOGIES;7.3 MANAGEMENT AND DECISION MAKING
Health categories: RESPIRATORY