Deep Learning-Based Quantification of Traction Bronchiectasis Severity For Predicting Outcome in Idiopathic Pulmonary Fibrosis-Reference-Cited by-同舟云学术

Deep Learning-Based Quantification of Traction Bronchiectasis Severity For Predicting Outcome in Idiopathic Pulmonary Fibrosis

Published:2023-10-24 Issue: Volume: Page:76-78
ISSN:2054-3166
Container-title:EMJ Respiratory
language:
Short-container-title:EMJ Respir

Author:

Felder F.¹,Nan Y.¹,Yang G.¹,Mackintosh J.²,Calandriello L.³,Goh N.⁴,Hopkins P.⁵,Moodley Y.⁶,Reynolds P.⁷,Corte T.⁸,Navaratnam V.⁹,Walsh S.¹

Affiliation:

1. National Heart and Lung Institute, Imperial College London, UK

2. Queensland Lung Transplant Service, The Prince Charles Hospital, Brisbane, Australia

3. Department of Diagnostic Imaging, Radiotherapy, Oncology, and Hematology, Agostino Gemelli University Policlinic, Rome, Italy; Istituto di Ricovero e Cura a Carattere Scientifico (IRCSS), Rome, Italy

4. Department of Respiratory and Sleep Medicine, Austin Health, Melbourne, Australia; Institute for Breathing and Sleep, Melbourne, Australia; University of Melbourne, Australia

5. Queensland Lung Transplant Service, The Prince Charles Hospital, Brisbane, Australia; Faculty of Medicine, University of Queensland, Brisbane, Australia

6. School of Medicine and Pharmacology, University of Western Australia, Perth, Australia

7. Royal Adelaide Hospital Chest Clinic, Adelaide, Australia

8. Respiratory Medicine, Royal Prince Alfred Hospital, Sydney, Australia

9. Department of Respiratory Medicine, Sir Charles Gardiner Hospital, Perth, Western Australia, Australia; University of Western Australia Medical School, Western Australia, Australia

Publisher

European Medical Group

Subject

General Medicine

Reference11 articles.

1. Flaherty KR et al.; INBUILD Trial Investigators. Nintedanib in progressive fibrosing interstitial lung diseases. New Engl J Med. 2019;381(18):1718-27.

2. Walsh SLF et al. Deep learning-based outcome prediction in progressive fibrotic lung disease using high-resolution computed tomography. Am J Respir Crit Care Med. 2022;206(7):883-91.

3. Nasser M et al. Progressive fibrosing interstitial lung disease: a clinical cohort (the PROGRESS study). Eur Respir J. 2021;57(2):2002718.

4. Takei R et al. Prevalence and prognosis of chronic fibrosing interstitial lung diseases with a progressive phenotype. Respirology. 2022;27(5):333-40.

5. Walsh SLF et al. Deep learning for classifying fibrotic lung disease on high-resolution computed tomography: a case-cohort study. Lancet Respir Med. 2018;6(11):837-45.