Machine learning in radiology: the new frontier in interstitial lung diseases. Issue 1 (January 2023)

• Record Type: Journal Article
• Authors: Barnes, Hayley; Humphries, Stephen M; George, Peter M; Assayag, Deborah; Glaspole, Ian; Mackintosh, John A; Corte, Tamera J; Glassberg, Marilyn; Johannson, Kerri A; Calandriello, Lucio; Felder, Federico; Wells, Athol; Walsh, Simon
• Is Part Of: Lancet. Volume 5:Issue 1(2023)
• Page Start: e41
• Page End: e50
• Publication Date: 2023-01
• Subjects: Medical care -- Data processing -- Periodicals; Medical care -- Information technology -- Periodicals; Medical informatics -- Periodicals; 610.285
• DOI: 10.1016/S2589-7500(22)00230-8
• Languages: English
• ISSNs: 2589-7500

Abstract

Summary: 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.