aceDevelopment and evaluation of a deep learning model for computer-aided diagnosis of neonatal pneumothorax on chest radiographs
Background Neonatal pneumothorax can progress rapidly, yet detection on supine chest radiographs remains challenging. Objective To develop and evaluate an artificial intelligence model for detecting pneumothorax on supine neonatal chest radiographs. Materials and methods This retrospective single-center study included neonates admitted to the neonatal intensive care unit between January 2011 and December 2024. The dataset comprised 648 radiographs from 288 neonates with pneumothorax and 5,511 radiographs from 3,…
A ResNet-18-based model trained on neonatal ICU radiographs detected pneumothorax on supine chest radiographs with AUC 0.975, 87.6% sensitivity and 95.3% specificity in the test set.
Model performance may be influenced by underlying pulmonary abnormalities, and lung-level localization was substantially lower for left-lung pneumothorax at 67.6% compared to right-lung.
Single-center retrospective design limits generalizability, and underlying pulmonary abnormalities may degrade performance; authors state external validation is needed before clinical use.
Evidence
- Peer-reviewedPediatric Radiology2026-09-12
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Truvace Impact Record TRV-2026-1085, v1: “Development and evaluation of a deep learning model for computer-aided diagnosis of neonatal pneumothorax on chest radiographs.” Truvace, 2026-09-14. /record/TRV-2026-1085 (accessed at citation time). sha256 57e83cdb4b8a821a…
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