TY - JOUR
T1 - Deep-Learning Algorithm Diagnostic Support for Usual Interstitial Pneumonia Pattern Recognition in Fibrotic Interstitial Lung Disease
AU - the SOFIA Project Consortium
AU - Fermoyle, Caitlin C.
AU - Mackintosh, John A.
AU - Navaratnam, Vidya
AU - Ellis, Samantha J.
AU - Cooper, Wendy A.
AU - Goh, Nicole S.L.
AU - Moodley, Yuben
AU - Reynolds, Paul N.
AU - Zappala, Christopher J.
AU - Hopkins, Peter
AU - Glaspole, Ian N.
AU - Corte, Tamera J.
AU - Walsh, Simon L.F.
AU - Accornero, Daniele
AU - Agrawal, Aditya
AU - Akilli, Isil Kibar
AU - Alamoudi, Omer
AU - Alberti, Maria Laura
AU - Aliannejad, Rasoul
AU - Aljahdali, Hamdan
AU - Amanda, Gina
AU - Anconina, Reut
AU - Antuni, Julio Daniel
AU - Aquaro, Giuseppe
AU - Arenas-Jimenez, Juan
AU - Asuquo, Bassey
AU - Au-Yong, Iain
AU - Avdeev, Sergey
AU - Balbi, Maurizio
AU - Baldi, Bruno
AU - Balestro, Elisabetta
AU - Ban, Andrea Yu Lin
AU - Bardaka, Fotini
AU - Bariga, Nicola Boscolo
AU - Baruah, Dhiraj
AU - Belaconi, Ionela
AU - Bendstrup, Elisabeth
AU - Bennett, David
AU - Blum, Hans Christian
AU - Bocchino, Marialuisa
AU - De Bontridder, Samuel
AU - Borghesi, Andrea
AU - Bouros, Demosthenes
AU - Bozovic, Gracijela
AU - Brillet, Pierre Yves
AU - Broqi, Marsel
AU - Bruzzi, John
AU - Buddha, Suryakala
AU - Buendia-Roldan, Ivette
AU - Cabo, Carolina
N1 - Publisher Copyright:
© 2026 The Author(s). Respirology published by John Wiley & Sons Australia, Ltd on behalf of Asian Pacific Society of Respirology.
PY - 2026/7/1
Y1 - 2026/7/1
N2 - 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 (ICCpre = 0.54[0.50–0.60]; ICCpost = 0.70[0.66–0.74]), and for probable UIP from fair to moderate (ICCpre = 0.30[0.27–0.35]; ICCpost = 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.
AB - 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 (ICCpre = 0.54[0.50–0.60]; ICCpost = 0.70[0.66–0.74]), and for probable UIP from fair to moderate (ICCpre = 0.30[0.27–0.35]; ICCpost = 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.
KW - deep learning
KW - disease progression
KW - idiopathic pulmonary fibrosis
KW - radiology
KW - usual interstitial pneumonia
UR - https://www.scopus.com/pages/publications/105035594968
U2 - 10.1002/resp.70246
DO - 10.1002/resp.70246
M3 - Article
C2 - 41923409
AN - SCOPUS:105035594968
SN - 1323-7799
VL - 31
SP - 711
EP - 720
JO - Respirology
JF - Respirology
IS - 7
ER -
SDG 3 Good Health and Well-being