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An artificial intelligence (AI) model can quantify lung fibrosis on CT scans, improving survival predictions for pulmonary hypertension (PH) patients when combined with standard scoring.

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Area of Science:

  • Radiology
  • Artificial Intelligence
  • Cardiology

Background:

  • Pulmonary hypertension (PH) requires better methods to assess disease severity, especially in idiopathic pulmonary arterial hypertension (IPAH) and PH with lung disease (PH-LD).
  • Current methods for quantifying lung disease in PH patients have limitations in predicting outcomes.

Purpose of the Study:

  • To use an artificial intelligence (AI) model to quantify lung fibrosis on CT pulmonary angiograms.
  • To evaluate if AI-quantified fibrosis, combined with radiologic scoring, can predict patient survival in PH.

Main Methods:

  • Retrospective multicenter study of adult patients with IPAH or PH-LD undergoing CT imaging.
  • Fibrosis quantified using an AI model and by radiologists; data split into training and external test cohorts.
  • Multivariable Cox regression and concordance index (C index) used to assess predictive performance.

Main Results:

  • AI-quantified lung fibrosis was significantly associated with increased mortality risk in both training and external test cohorts.
  • A model combining AI-quantified fibrosis and radiologic scoring demonstrated superior performance in predicting mortality (C index, 0.67) compared to radiologic scoring alone (C index, 0.61).

Conclusions:

  • AI-quantified lung fibrosis on CT pulmonary angiograms is a significant predictor of mortality in PH patients.
  • Combining AI-derived fibrosis quantification with radiologic scoring enhances the prediction of patient survival in PH.