Deep Learning-based Segmentation of CT Scans Predicts Disease Progression and Mortality in IPF

Muhunthan Thillai; Justin M. Oldham; Alessandro Ruggiero; Fahdi Kanavati; Tom M. McLellan; Gauri Saini; Simon R. Johnson; François-Xavier Blé; Adnan Azim; Kristoffer Ostridge; Adam Platt; Maria G. Belvisi; Toby M. Maher; Philip L. Molyneaux

doi:10.1164/rccm.202311-2185oc

March 7, 2024American Journal of Respiratory and Critical Care MedicineOpen Access

Deep Learning-based Segmentation of CT Scans Predicts Disease Progression and Mortality in IPF

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Key Points

Deep learning-based segmentation of computed tomography scans predicts disease progression and mortality in idiopathic pulmonary fibrosis patients.
Computed tomography scans provide crucial prognostic data in idiopathic pulmonary fibrosis, yet automated image-based biomarkers remain rarely used.
Automated segmentation using deep learning may enable routine adoption of computed tomography biomarkers within clinical practice and drug trials.

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Implication

Deep learning analysis demonstrates predictive value of CT scans in idiopathic pulmonary fibrosis, highlighting potential for clinical biomarkers.

Abstract

Despite evidence demonstrating a prognostic role for computed tomography (CT) scans in idiopathic pulmonary fibrosis (IPF), image-based biomarkers are not routinely used in clinical practice or trials.

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Thillai et al. (2024) studied this question.

synapsesocial.com/papers/68e7542bb6db6435876cbe28 https://doi.org/10.1164/rccm.202311-2185oc

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Deep Learning-based Segmentation of CT Scans Predicts Disease Progression and Mortality in IPF

Key Points

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Implication

Abstract

Discussion

Cite This Study

Deep Learning-based Segmentation of CT Scans Predicts Disease Progression and Mortality in IPF

Key Points

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Implication

Abstract

Discussion

Cite This Study

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