AI-Driven Assessment of Fibrotic and Vascular Changes Correlates with Patient Outcomes in Idiopathic Pulmonary Fibrosis
By
Julien Guiot
Jonne Engelberts
Monique Henket
Benoit Ernst
Quentin Maloir
Renaud Louis
David A. Lynch
Stephen M. Humphries
Jean-Paul Charbonnier
October 24, 2025
Clinical Scorecard: AI-Driven Assessment of Fibrotic and Vascular Changes Correlates with Patient Outcomes in Idiopathic Pulmonary Fibrosis
At a Glance
Category Detail
Condition Key Mechanisms AI-based imaging biomarkers for assessing fibrotic and vascular changes, improving diagnostic accuracy. Target Population Care Setting
Key Highlights
AI enhances the assessment of disease severity in IPF through quantitative imaging biomarkers. The GAP score is utilized for staging IPF severity. Pulmonary hypertension is a significant complication associated with IPF. Longitudinal studies are necessary for evaluating disease progression and treatment response, particularly in relation to anti-fibrotic therapies. AI models can differentiate IPF from other interstitial lung diseases. Guideline-Based Recommendations
Diagnosis
Management
Consider anti-fibrotic therapies such as nintedanib or pirfenidone for IPF patients. Monitoring & Follow-up
Risks
Patient & Prescribing Data
AI-driven tools can provide personalized assessments by analyzing patient-specific data and improving treatment outcomes.
Clinical Best Practices
Incorporate AI imaging tools such as LungQ into routine clinical practice for IPF assessment. Use the GAP score for staging and monitoring disease severity. Conduct longitudinal follow-up assessments to track disease progression. References
