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Computed tomography machine learning classifier correlates with mortality in interstitial lung disease.

Onofre Moran-Mendoza1, Abhishek Singla2, Angad Kalra3

  • 1Interstitial Lung Diseases Program, Division of Respirology and Sleep Medicine, Queen's University, 102 Stuart Street, Kingston, Ontario, K7L 2V7, Canada.

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|May 21, 2024
PubMed
Summary
This summary is machine-generated.

The Fibresolve machine learning system predicts mortality in interstitial lung diseases (ILDs) using CT scans. This tool offers prognostic performance comparable to the GAP score, aiding in patient outcome prediction.

Keywords:
Artificial intelligenceIdiopathic pulmonary fibrosisInterstitial lung diseaseMachine learning

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Area of Science:

  • Pulmonary Medicine
  • Radiology
  • Artificial Intelligence in Healthcare

Background:

  • Idiopathic pulmonary fibrosis (IPF) diagnosis often requires invasive procedures.
  • A novel machine learning system, Fibresolve, was developed for non-invasive diagnosis of IPF using chest CT imaging.
  • The study investigated Fibresolve's potential as a mortality predictor in interstitial lung diseases (ILDs).

Purpose of the Study:

  • To assess the predictive value of the Fibresolve classifier for mortality in patients with IPF and other ILDs.
  • To compare Fibresolve's prognostic performance against established risk factors and scoring systems.

Main Methods:

  • Fibresolve, a deep learning algorithm analyzing chest CT scans, was previously validated.
  • A subset of 228 patients with ILDs and available follow-up data was analyzed.
  • Cox regression analysis was performed, adjusting for the Gender, Age, and Physiology (GAP) score and other mortality predictors.

Main Results:

  • During a median follow-up of 2.8 years, 89 deaths occurred.
  • The Fibresolve score independently predicted mortality risk (HR: 7.14; p=0.02) after adjusting for GAP score and other factors.
  • Fibresolve tertiles also significantly predicted mortality risk (p=0.027), with higher tertiles showing increased hazard ratios.

Conclusions:

  • The Fibresolve machine learning classifier is an independent predictor of mortality in ILDs.
  • Fibresolve demonstrates prognostic performance equivalent to the GAP score, using only CT imaging.
  • This AI-driven tool offers a non-invasive method for risk stratification in ILD patients.