AI analysis of one-year CT changes predicts disease progression and survival in fibrotic interstitial lung disease.
Key Details
- 1Researchers at National Jewish Health used an AI-based tool to detect subtle increases in lung fibrosis on serial CT scans.
- 2A 5% or more increase in fibrosis score over one year doubled the risk of death or lung transplant for patients.
- 3AI-derived fibrosis changes predicted steeper lung function decline, especially in patients with less severe baseline disease.
- 4The deep learning method, data-driven textural analysis (DTA), provided precise, objective fibrosis quantification.
- 5Results were validated with data from the Pulmonary Fibrosis Foundation Patient Registry, demonstrating generalizability.
- 6Quantitative CT analysis could be used for trial endpoints and real-world risk stratification.
Why It Matters
This study demonstrates the clinical potential of deep learning-based CT quantification for early risk identification and outcome prediction in fibrotic lung disease. It supports wider use of imaging AI as an objective, reproducible tool for prognosis and treatment decision-making in complex pulmonary disorders.
Source
EurekAlert
Related News
•EurekAlert
NIH Invests Additional $12.6M in USC-Led Imaging AI for Alzheimer's
NIH has renewed and expanded its support for a USC-led consortium developing AI to decode and treat Alzheimer's using imaging and genomic data.
•EurekAlert
USC Unveils Joint Biomedical Engineering Department Bridging Medicine, Engineering, and Imaging
USC's medical and engineering schools launch a joint biomedical engineering department to accelerate interdisciplinary research and innovation, including imaging and AI.
•EurekAlert
AI Predicts Risks for Outpatient Stem Cell Therapy in Myeloma
Researchers use machine learning to predict adverse events during stem cell therapy for multiple myeloma, improving outpatient safety.