Computer-Aided Pulmonary Fibrosis Detection Leveraging an Advanced Artificial Intelligence Triage and Notification Software

Kavitha C. Selvan, Angad Kalra, Joshua Reicher, Michael Muelly, Ayodeji Adegunsoye

Abstract


Background: Improvement in recognition and referral of pulmonary fibrosis (PF) is vital to improving patient outcomes within interstitial lung disease. We determined the performance metrics and processing time of an artificial intelligence triage and notification software, ScreenDx-LungFibrosis, developed to improve detection of PF.

Methods: ScreenDx-LungFibrosis was applied to chest computed tomography (CT) scans from multisource data. Device output (+/- PF) was compared to clinical diagnosis (+/- PF), and diagnostic performance was evaluated. Primary endpoints included device sensitivity and specificity > 80% and processing time < 4.5 min.

Results: Of 3,018 patients included, PF was present in 22.9%. ScreenDx-LungFibrosis detected PF with a sensitivity and specificity of 91.3% (95% confidence interval (CI): 89.0-93.3%) and 95.1% (95% CI: 94.2-96.0%), respectively. Mean processing time was 27.6 s (95% CI: 26.0 - 29.1 s).

Conclusions: ScreenDx-LungFibrosis accurately and reliably identified PF with a rapid per-case processing time, underscoring its potential for transformative improvement in PF outcomes when routinely applied to chest CTs.




J Clin Med Res. 2023;15(8-9):423-429
doi: https://doi.org/10.14740/jocmr5020

Keywords


Pulmonary fibrosis; Interstitial lung disease; Early detection; Artificial intelligence

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