ArticleScientific reports2022
Diagnostic performance of artificial intelligence approved for adults for the interpretation of pediatric chest radiographs.
Article in Scientific reports, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 32 papers, 1 of them a synthesis that pooled it.
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32 citing papers in PubMed, 1 synthesis or guideline pooled it, 40 citations in OpenAlex.
- Deep learning for pneumothorax diagnosis: a systematic review and meta-analysis.European respiratory review : an official journal of the European Respiratory Society · 2023Pooled it
- Repurposing adult AI radiograph models for neonatal care through continual learning: an international multicentre study.Nature biomedical engineering · 2026Article
- Development and evaluation of a deep learning model for computer-aided diagnosis of neonatal pneumothorax on chest radiographs.Pediatric radiology · 2026Article
- Current applications and challenges of artificial intelligence applied to diagnostics in pediatric musculoskeletal imaging.Pediatric radiology · 2026Review
- Artificial intelligence in paediatric chest imaging: applications, challenges, and future directions.Pediatric radiology · 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
- Data mining in pediatric radiology in the era of artificial intelligence.Pediatric radiology · 2026Review
- Current State and Future Directions of Diagnostic Imaging and Interpretation in Pediatric Radiology.Diagnostics (Basel, Switzerland) · 2026Article
- Performance of an adult-trained AI tool for intracranial hemorrhage detection on head CT in children aged 6-17 years.Pediatric radiology · 2026Article
- Bridging the gap: the need for pediatric-specific AI models for tuberculosis detection on chest radiographs.Annals of medicine and surgery (2012) · 2026Article
- AI-driven diagnosis of pneumococcal infections in children-a call for primary-care focused and a coherent evidence base.Frontiers in artificial intelligence · 2026Review
- Pediatric pulmonary nodules: current state of knowledge, AI applications, and future directions.Pediatric radiology · 2026Review
- External Validation of an Upgraded AI Model for Screening Ileocolic Intussusception Using Pediatric Abdominal Radiographs: Multicenter Retrospective Study.Journal of medical Internet research · 2025Article
- Children Are Not Small Adults: Addressing Limited Generalizability of an Adult Deep Learning CT Organ Segmentation Model to the Pediatric Population.Journal of imaging informatics in medicine · 2025Article
- Ethical considerations in AI for child health and recommendations for child-centered medical AI.NPJ digital medicine · 2025Review
- Deep learning for pediatric chest x-ray diagnosis: Repurposing a commercial tool developed for adults.PloS one · 2025Article
- Optimizing adult-oriented artificial intelligence for pediatric chest radiographs by adjusting operating points.Scientific reports · 2024Article
- Artificial intelligence: a primer for pediatric radiologists.Pediatric radiology · 2024Review
- Acute Respiratory Failure in Children: A Clinical Update on Diagnosis.Children (Basel, Switzerland) · 2024Review
Corrections and comments
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Authors and funding
4 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Artificial intelligence (AI) applied to pediatric chest radiographs are yet scarce. This study evaluated whether AI-based software developed for adult chest radiographs can be used for pediatric chest radiographs. Pediatric patients (≤ 18 years old) who underwent chest radiographs from March to May 2021 were included retrospectively. An AI-based lesion detection software assessed the presence of nodules, consolidation, fibrosis, atelectasis, cardiomegaly, pleural effusion, pneumothorax, and pneumoperitoneum. Using the pediatric radiologist's results as standard reference, we assessed the diagnostic performance of the software. For the total 2273 chest radiographs, the AI-based software showed a sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and accuracy of 67.2%, 91.1%, 57.7%, 93.9%, and 87.5%, respectively. Age was a significant factor for incorrect results (odds radio 0.821, 95% confidence interval 0.791-0.851). When we excluded cardiomegaly and children 2 years old or younger, sensitivity, specificity, PPV, NPV and accuracy significantly increased (86.4%, 97.9%, 79.7%, 98.7% and 96.9%, respectively, all p < 0.001). In conclusion, AI-based software developed with adult chest radiographs showed diagnostic accuracies up to 96.9% for pediatric chest radiographs when we excluded cardiomegaly and children 2 years old or younger. AI-based lesion detection software needs to be validated in younger children.
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