AI4LUNGS | AI-BASED PERSONALISED CARE FOR RESPIRATORY DISEASE USING MULTI-MODAL DATA IN PATIENT STRATIFICATION

Original description

AI4Lungs will develop and validate novel AI-based tools and computational models to improve patient stratification optimising diagnosis and treatment of infectious and non-infectious respiratory diseases. Diagnosis of respiratory disease comprises a complex assessment of several multiple exams over time that together characterise the patient condition. Streamlined into existing clinical pathways, AI4Lungs will support clinicians and other stakeholders in decision making along the patient journey from initial suspicion to diagnosis, and treatment planning. The models incorporate clinical partners’ multiple data sources, registries and open national/international databases, including multiple data types from medical records, imaging data as well as novel data from digital stethoscope and –omics. AI4Lungs stratification strategy will build computational models employing structured and unstructured data modalities, leading to more accurate positioning of patients and enabling them to benefit from global data and knowledge shared during all stages of care, focusing on diagnosis and treatment planning. With scale up, AI4Lungs will support any patient from any country, any hospital no matter how remote or small, by stratifying them among all of Europe’s patients from that cluster, gaining access to the collective expertise, experience and information on optimal care options. In parallel, health systems will reduce costs and effort in unnecessary testing, ineffective treatments and emergency services, optimizing use of health technologies and resources. AI4Lungs patient stratification tools will focus on respiratory diseases, a complex and broad set of disorders with high disease burden. AI and real world data combined with innovative holistic diseases modelling, will offer a solution for allocating resources more efficiently, making best treatment pipelines accessible to more patients while complying with FAIR principles and relevant regulatory and ethical guidelines.

Status

SIGNED

Url

https://cordis.europa.eu/project/id/101080756

Call topic

HORIZON-HLTH-2022-TOOL-12-01-two-stage

Update Date

12-03-2024