ArticleKorean journal of radiology2022
Deep Learning-Assisted Diagnosis of Pediatric Skull Fractures on Plain Radiographs.
Article in Korean journal of radiology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers, 4 of them syntheses that pooled it.
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Who cites it
26 citing papers in PubMed, 4 syntheses or guidelines pooled it, 41 citations in OpenAlex.
- Enhanced fracture detection on radiographs with AI assistance for clinicians: a systematic review and meta-analysis.Annals of medicine · 2026Pooled it
- Diagnostic accuracy of AI-assisted versus independent physician interpretation for bone fractures: a systematic review and meta-analysis.European radiology experimental · 2026Pooled it
- Diagnostic performance of neural network algorithms in skull fracture detection on CT scans: a systematic review and meta-analysis.Emergency radiology · 2025Pooled it
- Diagnostic and prognostic applications of machine learning in paediatric traumatic brain injury: a systematic review of single and multimodal approaches.Frontiers in neurologyPooled it
- Automated Diagnosis of Infantile Skull Fractures From X-Ray Images Using an Ensemble Deep Learning Model.Journal of Korean medical science · 2026Article
- Harnessing artificial intelligence for pediatric health: Current trends and future opportunities.iScience · 2026Review
- Artificial intelligence based sonographic differentiation between skull fractures and normal sutures in young children.Scientific reports · 2025Article
- Crucial Role of Understanding in Human-Artificial Intelligence Interaction for Successful Clinical Adoption.Korean journal of radiology · 2025Article
- Artificial Intelligence Application in Skull Bone Fracture with Segmentation Approach.Journal of imaging informatics in medicine · 2025Article
- Deep learning model using cross-sequence learning to identify orbital fractures in radiographs of patients under 20 Years.Frontiers in bioengineering and biotechnology · 2025Article
- Optimizing adult-oriented artificial intelligence for pediatric chest radiographs by adjusting operating points.Scientific reports · 2024Article
- Classification of AO/OTA 31A/B femur fractures in X-ray images using YOLOv8 and advanced data augmentation techniques.Bone reports · 2024Article
- Reporting Guidelines for Artificial Intelligence Studies in Healthcare (for Both Conventional and Large Language Models): What's New in 2024.Korean journal of radiology · 2024Article
- The Quantitative Evaluation of Automatic Segmentation in Lumbar Magnetic Resonance Images.Neurospine · 2024Article
- Survey on Value Elements Provided by Artificial Intelligence and Their Eligibility for Insurance Coverage With an Emphasis on Patient-Centered Outcomes.Korean journal of radiology · 2024Article
- The Role of Artificial Intelligence in the Identification and Evaluation of Bone Fractures.Bioengineering (Basel, Switzerland) · 2024Review
- Reporting Quality of Research Studies on AI Applications in Medical Images According to the CLAIM Guidelines in a Radiology Journal With a Strong Prominence in Asia.Korean journal of radiology · 2023Review
- Developing and Evaluating Deep Learning Algorithms for Object Detection: Key Points for Achieving Superior Model Performance.Korean journal of radiology · 2023Review
- Systematic review of the performance evaluation of clinicians with or without the aid of machine learning clinical decision support system.Health and technology · 2023Review
- Artificial intelligence CAD tools in trauma imaging: a scoping review from the American Society of Emergency Radiology (ASER) AI/ML Expert Panel.Emergency radiology · 2023Article
Corrections and comments
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Authors and funding
12 authors at 7 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectiveTo develop and evaluate a deep learning-based artificial intelligence (AI) model for detecting skull fractures on plain radiographs in children. MATERIALS AND
methodsThis retrospective multi-center study consisted of a development dataset acquired from two hospitals (n = 149 and 264) and an external test set (n = 95) from a third hospital. Datasets included children with head trauma who underwent both skull radiography and cranial computed tomography (CT). The development dataset was split into training, tuning, and internal test sets in a ratio of 7:1:2. The reference standard for skull fracture was cranial CT. Two radiology residents, a pediatric radiologist, and two emergency physicians participated in a two-session observer study on an external test set with and without AI assistance. We obtained the area under the receiver operating characteristic curve (AUROC), sensitivity, and specificity along with their 95% confidence intervals (CIs).
resultsThe AI model showed an AUROC of 0.922 (95% CI, 0.842-0.969) in the internal test set and 0.870 (95% CI, 0.785-0.930) in the external test set. The model had a sensitivity of 81.1% (95% CI, 64.8%-92.0%) and specificity of 91.3% (95% CI, 79.2%-97.6%) for the internal test set and 78.9% (95% CI, 54.4%-93.9%) and 88.2% (95% CI, 78.7%-94.4%), respectively, for the external test set. With the model's assistance, significant AUROC improvement was observed in radiology residents (pooled results) and emergency physicians (pooled results) with the difference from reading without AI assistance of 0.094 (95% CI, 0.020-0.168;
conclusionA deep learning-based AI model improved the performance of inexperienced radiologists and emergency physicians in diagnosing pediatric skull fractures on plain radiographs.
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Registered trials
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.