Interstitial lung disease pattern recognition on full high resolution computed tomography volume: Development and evaluation of a decision-support tool for less-experimented radiologists.

Authors

Valentin Ong, Rafael Marini, Raphael Borie, Constance De Margerie-Mellon, Mathieu Lederlin, Samia Boussouar, Nisrine Chalhoub, Sarra Sahraoui, Denis Habip Gatenyo, Nicolas Billet, Penelope Gaillot, Sara Benchara, Ghita Miyara, Camille-Albane Pincet, Francesco Dellavalle, Jean-Baptiste Safa, Luc Mouthon, Bruno Crestani, Isabelle Honore, Abdellatif Tazi, Gwenaël Lorillon, Marie-Pierre Debray, Guillaume Chassagnon

Published in

Diagnostic and interventional imaging. Jun 27, 2026. Epub Jun 27, 2026.

Abstract

The purpose of this study was to develop an artificial intelligence (AI) tool to assist recognition of three major interstitial lung disease (ILD) patterns on high-resolution computed tomography (HRCT) and to evaluate its added value in supporting decision-making for non-specialist radiologists.
This retrospective, multicenter study included 1097 HRCT examinations. Of these, 989 (90.15%) were used for development and 108 (9.85%) for external testing. A two-stage architecture inspired by domain-specific pretraining was employed. The encoder of a three-dimensional ILD segmentation model was kept to extract 7168 disease-specific features per HRCT, which were combined with age and sex in a deep learning model to predict three radiological patterns (usual interstitial pneumonia, non-specific interstitial pneumonia and fibrotic bronchiolocentric interstitial pneumonia) as diagnosed in multidisciplinary discussions (MDD). The external test dataset was interpreted by seven thoracic radiologists to establish a second reference (majority's vote) and by eight radiology residents with and without AI assistance. Accuracy, sensitivity and specificity were calculated for each pattern.
The AI system achieved 77.8% accuracy on the external test dataset using MMD as a reference standard, within the range of thoracic experts (median, 75.6%; range: 61.1-81.5). AI assistance improved residents' median accuracy (+14.8% of absolute increase) and reduced reading time by 20.7% (P < 0.001). Six out of eight residents assisted by AI (75%) performed worse than AI alone.
AI can accurately classify major ILD patterns and help less-experienced readers improve their performance. However, the level of improvement was inconsistent, and non-specialists rarely equaled the performance of AI alone.

PMID:
42364929
Bibliographic data and abstract were imported from PubMed on 28 Jun 2026.

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