ArticleScientific reports2024
Optimizing adult-oriented artificial intelligence for pediatric chest radiographs by adjusting operating points.
Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed.
- Letter to the Editor: Commercial versus open-source artificial intelligence in paediatric imaging-closing the stratified analysis gap in a scoping review.European radiology · 2026Article
- Artificial intelligence-enabled pediatric radiology in low-resource settings: addressing resource constraints in the African healthcare system.Pediatric radiology · 2026Review
- Artificial Intelligence in Pediatric Imaging: A Primer for Pediatric Clinicians.Indian journal of pediatrics · 2026Review
- Foundation models in radiology: a primer for pediatric radiologists.Pediatric radiology · 2026Review
- Performance of adult-trained artificial intelligence models in paediatric imaging-a scoping review.European radiology · 2026Article
- Barriers to implementing AI in pediatric cancer imaging.Cancer imaging : the official publication of the International Cancer Imaging Society · 2026Article
- How threshold customisation affects the performance of a multiclass X-ray AI model for primary care triage: a retrospective study.BMJ open · 2026Article
- Enhancing post-kidney transplant prognostication: an interpretable machine learning approach for longitudinal outcome prediction.NPJ digital medicine · 2025Article
- Lack of children in public medical imaging data points to growing age bias in biomedical AI.Research square · 2025Article
- Threshold optimization in AI chest radiography analysis: integrating real-world data and clinical subgroups.European radiology experimental · 2025Article
- Lack of children in public medical imaging data points to growing age bias in biomedical AI.medRxiv : the preprint server for health sciences · 2025Article
- Deep learning for pediatric chest x-ray diagnosis: Repurposing a commercial tool developed for adults.PloS one · 2025Article
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7 authors.
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Abstract
The purpose of this study was to evaluate whether the optimal operating points of adult-oriented artificial intelligence (AI) software differ for pediatric chest radiographs and to assess its diagnostic performance. Chest radiographs from patients under 19 years old, collected between March and November 2021, were divided into test and exploring sets. A commercial adult-oriented AI software was utilized to detect lung lesions, including pneumothorax, consolidation, nodule, and pleural effusion, using a standard operating point of 15%. A pediatric radiologist reviewed the radiographs to establish ground truth for lesion presence. To determine the optimal operating points, receiver operating characteristic (ROC) curve analysis was conducted, varying thresholds to balance sensitivity and specificity by lesion type, age group, and imaging method. The test set (4,727 chest radiographs, mean 7.2 ± 6.1 years) and exploring set (2,630 radiographs, mean 5.9 ± 6.0 years) yielded optimal operating points of 11% for pneumothorax, 14% for consolidation, 15% for nodules, and 6% for pleural effusion. Using a 3% operating point improved pneumothorax sensitivity for children under 2 years, portable radiographs, and anteroposterior projections. Therefore, optimizing operating points of AI based on lesion type, age, and imaging method could improve diagnostic performance for pediatric chest radiographs, building on adult-oriented AI as a foundation.
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