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

Source
EurekAlert
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