Deep learning for pediatric chest x-ray diagnosis: Repurposing a commercial tool developed for adults

Prerana Agarwal, Alexander Rau, Helen Ngo, Ambika Seth, Fabian Bamberg, Elmar Kotter and Jakob Weiss

PLOS ONE, 2025, vol. 20, issue 7, 1-11

Abstract: The number of commercially available artificial intelligence (AI) tools to support radiological workflows is constantly increasing, yet dedicated solutions for children are largely unavailable. Here, we repurposed an AI-tool developed for chest radiograph interpretation in adults (Lunit INSIGHT CXR) and investigated its diagnostic performance in a real-world pediatric clinical dataset. 958 consecutive frontal chest radiographs of children aged 2−14 years were included and analyzed with the commercially available AI-tool. The reference standard was determined in a dedicated reading session by a board-certified radiologist. The original reports validated by specialized pediatric radiologists, were considered as second readings. All discordant findings were reanalyzed in consensus. The diagnostic performance of the AI-tool was validated using standard measures of accuracy. For this, the continuous AI output (ranging from 0−100) was binarized using vendor recommended thresholds recommended for adults and optimized thresholds identified for children. Relevant findings were defined as consolidation, atelectasis, nodule, cardiomegaly, mediastinal widening due to mass, pleural effusion and pneumothorax. 200 radiographs [20.9%] demonstrated at least one relevant pathology. Using the adult threshold, the AI-tool showed a high performance for all relevant findings with an AUC 0.94 (95% CI: 0.92–0.95) and. In stratified analysis by age (2−7 vs. 7–14-years-old) a significantly higher performance (p

Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:plo:pone00:0328295

DOI: 10.1371/journal.pone.0328295

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