Machine learning in radiology: the new frontier in interstitial lung diseases

Hayley Barnes; Stephen M Humphries; Peter M George; Deborah Assayag; Ian Glaspole; John A Mackintosh; Tamera J Corte; Marilyn Glassberg; Kerri A Johannson; Lucio Calandriello; Federico Felder; Athol Wells; Simon Walsh

doi:10.1016/S2589-7500(22)00230-8

Machine learning in radiology: the new frontier in interstitial lung diseases

Lancet Digit Health. 2023 Jan;5(1):e41-e50. doi: 10.1016/S2589-7500(22)00230-8. Epub 2022 Dec 12.

Authors

Affiliations

¹ Department of Respiratory Medicine, Alfred Health, Melbourne, VIC, Australia; Central Clinical School, Monash University, Melbourne, VIC, Australia; Centre for Occupational and Environmental Health, Monash University, Melbourne, VIC, Australia. Electronic address: hayley.barnes@monash.edu.
² Department of Radiology, National Jewish Health, Denver, CO, USA.
³ Interstitial Lung Disease Unit, Royal Brompton and Harefield Hospitals, London, UK; National Heart and Lung Institute, Imperial College London, London, UK.
⁴ Department of Medicine, McGill University, Montreal, QC, Canada.
⁵ Department of Respiratory Medicine, Alfred Health, Melbourne, VIC, Australia; Central Clinical School, Monash University, Melbourne, VIC, Australia.
⁶ Department of Thoracic Medicine, The Prince Charles Hospital, Brisbane, QLD, Australia.
⁷ Department of Respiratory Medicine, Royal Prince Alfred Hospital, Sydney, NSW, Australia; Central Clinical School, University of Sydney, Sydney, NSW, Australia.
⁸ Division of Pulmonary, Critical Care and Sleep Medicine, University of Arizona College of Medicine Phoenix, Phoenix, AR, USA.
⁹ Department of Medicine, University of Calgary, Calgary, AB, Canada.
¹⁰ Department of Diagnostic Imaging, Oncological Radiotherapy and Haematology, Fondazione Policlinico Universitario A Gemelli, IRCCS, Rome, Italy.
¹¹ National Heart and Lung Institute, Imperial College London, London, UK.

PMID: 36517410
DOI: 10.1016/S2589-7500(22)00230-8

Abstract

Challenges for the effective management of interstitial lung diseases (ILDs) include difficulties with the early detection of disease, accurate prognostication with baseline data, and accurate and precise response to therapy. The purpose of this Review is to describe the clinical and research gaps in the diagnosis and prognosis of ILD, and how machine learning can be applied to image biomarker research to close these gaps. Machine-learning algorithms can identify ILD in at-risk populations, predict the extent of lung fibrosis, correlate radiological abnormalities with lung function decline, and be used as endpoints in treatment trials, exemplifying how this technology can be used in care for people with ILD. Advances in image processing and analysis provide further opportunities to use machine learning that incorporates deep-learning-based image analysis and radiomics. Collaboration and consistency are required to develop optimal algorithms, and candidate radiological biomarkers should be validated against appropriate predictors of disease outcomes.

Publication types

Review

MeSH terms

Biomarkers
Humans
Lung Diseases, Interstitial* / diagnostic imaging
Lung Diseases, Interstitial* / therapy
Prognosis
Radiology*
Risk Factors

Substances

Biomarkers