SynthesisPediatric radiology2022
Pediatric chest radiograph interpretation: how far has artificial intelligence come? A systematic literature review.
Synthesis in Pediatric radiology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers.
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Who cites it
26 citing papers in PubMed.
- A two-stage deep learning pipeline for automated detection and localization of endotracheal tubes on pediatric chest radiographs: a pilot study.Pediatric radiology · 2026Article
- KD-SqueezeNet: an efficient deep learning strategy for the multi-task diagnosis of neonatal lung diseases.Pediatric radiology · 2026Article
- Artificial intelligence in paediatric chest imaging: applications, challenges, and future directions.Pediatric radiology · 2026Review
- Pediatric radiology informatics within a larger academic practice.Pediatric radiology · 2026Review
- Transforming Pediatric Care Through AI: Bridging the Digital Divide in Health Informatics.Online journal of public health informatics · 2026Article
- Advances in AI for detecting pulmonary inflammation and perioperative medicine: a mini-review.Frontiers in medicine · 2026Review
- Article
- AI-driven diagnosis of pneumococcal infections in children-a call for primary-care focused and a coherent evidence base.Frontiers in artificial intelligence · 2026Review
- NeoCLIP: a self-supervised foundation model for the interpretation of neonatal radiographs.NPJ digital medicine · 2025Article
- Accuracy of Large Language Models in Detecting Cases Requiring Immediate Reporting in Pediatric Radiology: A Feasibility Study Using Publicly Available Clinical Vignettes.Korean journal of radiology · 2025Article
- The Hidden Threat of Hallucinations in Binary Chest X-ray Pneumonia Classification.Proceedings. IEEE International Symposium on Computer-Based Medical Systems · 2025Article
- Deep learning for pediatric chest x-ray diagnosis: Repurposing a commercial tool developed for adults.PloS one · 2025Article
- NDL-Net: A Hybrid Deep Learning Framework for Diagnosing Neonatal Respiratory Distress Syndrome From Chest X-Rays.IEEE open journal of engineering in medicine and biology · 2025Article
- Optimizing adult-oriented artificial intelligence for pediatric chest radiographs by adjusting operating points.Scientific reports · 2024Article
- Addressing Class Imbalance with Latent Diffusion-based Data Augmentation for Improving Disease Classification in Pediatric Chest X-rays.Proceedings. IEEE International Conference on Bioinformatics and Biomedicine · 2024Article
- Artificial intelligence: a primer for pediatric radiologists.Pediatric radiology · 2024Review
- Artificial Intelligence (AI) Assessment of Pediatric Dental Panoramic Radiographs (DPRs): A Clinical Study.Pediatric reports · 2024Article
- Three-Stage Framework for Accurate Pediatric Chest X-ray Diagnosis Using Self-Supervision and Transfer Learning on Small Datasets.Diagnostics (Basel, Switzerland) · 2024Article
- Computer-Aided Lung Auscultation Screening and Radiographic Evaluation of Pediatric Pneumonia.Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference · 2024Article
- Evaluation of the Performance of an Artificial Intelligence (AI) Algorithm in Detecting Thoracic Pathologies on Chest Radiographs.Diagnostics (Basel, Switzerland) · 2024Article
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Most artificial intelligence (AI) studies have focused primarily on adult imaging, with less attention to the unique aspects of pediatric imaging. The objectives of this study were to (1) identify all publicly available pediatric datasets and determine their potential utility and limitations for pediatric AI studies and (2) systematically review the literature to assess the current state of AI in pediatric chest radiograph interpretation. We searched PubMed, Web of Science and Embase to retrieve all studies from 1990 to 2021 that assessed AI for pediatric chest radiograph interpretation and abstracted the datasets used to train and test AI algorithms, approaches and performance metrics. Of 29 publicly available chest radiograph datasets, 2 datasets included solely pediatric chest radiographs, and 7 datasets included pediatric and adult patients. We identified 55 articles that implemented an AI model to interpret pediatric chest radiographs or pediatric and adult chest radiographs. Classification of chest radiographs as pneumonia was the most common application of AI, evaluated in 65% of the studies. Although many studies report high diagnostic accuracy, most algorithms were not validated on external datasets. Most AI studies for pediatric chest radiograph interpretation have focused on a limited number of diseases, and progress is hindered by a lack of large-scale pediatric chest radiograph datasets.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.