Inflammation vs fibrosis
Redefine disease characterisation by distinguishing inflammatory from fibrotic profiles with integrated multimodal AI.
Patients with fibrosing interstitial lung disease (fILD) can progress through inflammation, fibrosis, or both. PRISM-fILD aims to develop a multimodal AI approach that combines CT images, radiology reports, clinical data and omics to distinguish inflammatory from fibrotic disease profiles, and to support personalised diagnosis and prognosis.
Interstitial lung diseases (ILDs) damage the thin tissue around the air sacs, where oxygen passes into the blood. In fibrosing ILD, this tissue is gradually replaced by scar tissue. The lungs become stiff, breathing gets harder, and in severe cases the disease can lead to respiratory failure.
The causes vary, from autoimmune conditions to genetic and environmental factors. The disease is usually driven by inflammation, fibrosis (scarring), or a mix of both, and telling them apart matters for treatment: anti-inflammatory drugs can help when inflammation dominates but may cause harm when fibrosis dominates.
PRISM-fILD aims to combine CT images, radiology reports, clinical data and omics, including follow-up over time. Multimodal AI will bring these pieces together to characterise each patient’s disease more precisely and to anticipate how it may develop, supporting doctors in personalised diagnosis and treatment decisions.
PRISM-fILD stands for Precision Medicine through Redefinition of Diagnosis and Management using Integrated Systems and Multilevel Data in fibrosing Interstitial Lung Diseases.
PRISM-fILD is a four-year research project funded by the Swiss National Science Foundation (SNSF). A multidisciplinary team of clinicians and imaging, pathology, biomarker and AI experts from Switzerland, Germany, Spain, the United Kingdom and the United States works together to advance precision medicine for patients with fibrosing ILD.
Can integrated multimodal analysis distinguish inflammatory from fibrotic disease, improve prognosis, and reveal potentially treatable traits?
Central research question of PRISM-fILDRedefine disease characterisation by distinguishing inflammatory from fibrotic profiles with integrated multimodal AI.
Explore omics signatures and biomarkers of inflammation and fibrosis, and identify potentially treatable traits.
Use longitudinal data to predict disease progression and support clinicians in anticipating each patient’s course.
A research workflow designed to turn multicentre patient data into validated AI decision support.
International cohorts and prospective Swiss recruitment
Pseudonymisation and secure, compliant data sharing
Curated, AI-ready multimodal database
MDD consensus, CT annotation and omics
Multimodal AI for characterisation and prognosis
External, longitudinal and prospective validation
Each type of patient information will be analysed on its own and then combined, aiming to give a fuller picture of each patient than any single source can provide.
Combines complementary information into one patient profile
PRISM-fILD is designed to support clinical judgement, never to replace it.
Patient data are pseudonymised and shared securely under role-based access, in line with Swiss data protection law (FADP), GDPR and HIPAA, and with ethical approval.
Multidisciplinary expert consensus, confirmed by the disease course and treatment response, defines the reference standard that the AI learns from and is measured against.
Results will indicate which information shaped each assessment, and AI errors will be analysed systematically. The AI is designed to support clinicians’ decisions and will be evaluated together with them.
Pulmonology, AI in medicine and thoracic radiology, backed by an international network of ILD experts.













Scientific publications, conference contributions, code and software arising from PRISM-fILD, listed as they become available.
Peer reviewed articles reporting the methods, findings and validation results of the project.
Abstracts, posters and oral presentations at national and international scientific meetings.
Open source tools and trained models developed within the project, released for reuse by the research community.
Clinical, imaging and AI partners from Europe and North America convened to formally launch the project.

An international collaboration, led from Switzerland, to advance precision medicine in fibrosing ILD.
Questions about PRISM-fILD or interested in collaborating? Contact the project leads directly.
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