U-net Convolutional Neural Network Applied to Progressive Fibrotic Interstitial Lung Disease: is Progression at CT Scan Associated with a Clinical Outcome?

Background: Computational advances in artificial intelligence have led to the recent emergence of U-Net con-volutional neural networks (CNNs) applied to medical imaging. Our objectives were to assess the progression of fibrotic interstitial lung disease (ILD) using routine CT scans processed by a U-Net CNN developed by our research team, and to identify a progression threshold indicative of poor prognosis.Methods: CT scans and clinical history of 32 patients with idiopathic fibrotic ILDs were retrospectively reviewed. Successive CT scans were processed by the U-Net CNN and ILD quantification was obtained. Corre-lation between ILD and FVC changes was assessed. ROC curve was used to define a threshold of ILD progres-sion rate (PR) to predict poor prognostic (mortality or lung transplantation). The PR threshold was used to compare the cohort survival with Kaplan Mayer curves and log-rank test.Results: The follow-up was 3.8 +/- 1.5 years encompassing 105 CT scans, with 3.3 +/- 1.1 CT scans per patient. A significant correlation between ILD and FVC changes was obtained (p = 0.004, r =-0.30 [95% CI:-0.16 to-0.45]). Sixteen patients (50%) experienced unfavorable outcome including 13 deaths and 3 lung transplanta-tions. ROC curve analysis showed an aera under curve of 0.83 (p < 0.001), with an optimal cut-off PR value of 4%/year. Patients exhibiting a PR >= 4%/year during the first two years had a poorer prognosis (p = 0.001).Conclusions: Applying a U-Net CNN to routine CT scan allowed identifying patients with a rapid progression and unfavorable outcome.(c) 2023 SPLF and Elsevier Masson SAS. All rights reserved.