Deep‐Learning Algorithm Diagnostic Support for Usual Interstitial Pneumonia Pattern Recognition in Fibrotic Interstitial Lung Disease
Year: 2026
Type: article
Abstract: ABSTRACT Background and Objective High resolution computed tomography (HRCT) scan diagnostic classification for usual interstitial pneumonia (UIP) plays a critical role in therapeutic decision‐making and clinical trial eligibility for interstitial lung disease (ILD) patients, but is subject to variability. A deep learning algorithm, the Systematic Objective Fibrotic Imaging Analysis Algorithm (SOFIA), has been validated to assist classification of HRCTs based on current guidelines. In this study, we evaluate the impact of SOFIA on inter‐observer agreement for UIP classification and prognostic accuracy of clinicians' assessment of ILD HRCTs. Methods Radiologists and pulmonologists (reviewers) were invited to evaluate 203 HRCTs from a national fibrotic ILD registry, scoring each of four UIP categories (definite UIP, probable UIP, indeterminate, or alternative diagnosis). SOFIA outputs were then provided, and reviewers were able to reevaluate their scores. Changes in interobserver agreement for UIP classification and prognostic accuracy were calculated. Results Three hundred twelve reviewers (120 radiologists, 192 pulmonologists) from 49 countries evaluated 203 HRCT scans. Following SOFIA, inter‐observer diagnostic agreement improved for definite UIP from moderate to good (ICC pre = 0.54[0.50–0.60]; ICC post = 0.70[0.66–0.74]), and for probable UIP from fair to moderate (ICC pre = 0.30[0.27–0.35]; ICC post = 0.53[0.49–0.58]). Following SOFIA, there was improved prognostic accuracy for reviewers' definite UIP, probable UIP, and indeterminate scores (significant change in c‐index), and the proportion of reviewers whose probable UIP scores were significantly predictive of transplant‐free survival increased by 42%. Conclusion Providing SOFIA algorithm output to clinicians reviewing HRCT scans improved diagnostic agreement and prognostic accuracy for fibrotic ILD. SOFIA may be a useful automated assistive tool to support improved diagnostic consistency.
Source: Respirology
DOI: 10.1002/resp.70246
Institutions: The University of Sydney, The University of Queensland, The University of Western Australia, Sir Charles Gairdner Hospital, Institute for Respiratory Health, The Alfred Hospital, Monash University, Royal Prince Alfred Hospital, NSW Health Pathology, Western Sydney University, The University of Melbourne, Austin Hospital, Institute for Breathing and Sleep, Royal Perth Hospital, Royal Adelaide Hospital, The University of Adelaide, Imperial College London
Language: English
Subfield: Pulmonary and Respiratory Medicine
Field: Medicine
Domain: Health Sciences
Open Access status: hybrid
Estimated APC paid: $4,330
OpenAlex ID: W7147621342
