ArticleRadiology. Artificial intelligence2019
Improving Automated Pediatric Bone Age Estimation Using Ensembles of Models from the 2017 RSNA Machine Learning Challenge.
Article in Radiology. Artificial intelligence, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers, 1 of them a synthesis that pooled it.
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26 citing papers in PubMed, 1 synthesis or guideline pooled it, 55 citations in OpenAlex.
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- A Novel Dual-Output Deep Learning Model Based on InceptionV3 for Radiographic Bone Age and Gender Assessment.Journal of imaging informatics in medicine · 2026Article
- Automatic bone age assessment: a Turkish population study.Diagnostic and interventional radiology (Ankara, Turkey) · 2025Article
- Predicting pediatric age from chest X-rays using deep learning: a novel approach.Insights into imaging · 2025Article
- A review of methods of age estimation based on postmortem computed tomography.Forensic sciences research · 2025Review
- BAE-ViT: An Efficient Multimodal Vision Transformer for Bone Age Estimation.Tomography (Ann Arbor, Mich.) · 2024Article
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- The automated Greulich and Pyle: a coming-of-age for segmental methods?Frontiers in artificial intelligence · 2024Article
- Collaborative Privacy-preserving Approaches for Distributed Deep Learning Using Multi-Institutional Data.Radiographics : a review publication of the Radiological Society of North America, Inc · 2023Article
- PredictingFrontiers in microbiology · 2023Article
- Current and emerging artificial intelligence applications for pediatric musculoskeletal radiology.Pediatric radiology · 2022Review
- The current and future roles of artificial intelligence in pediatric radiology.Pediatric radiology · 2022Review
- A Crowdsourcing Approach to Develop Machine Learning Models to Quantify Radiographic Joint Damage in Rheumatoid Arthritis.JAMA network open · 2022Article
- Accuracy and self-validation of automated bone age determination.Scientific reports · 2022Article
- Assessment of rapidly advancing bone age during puberty on elbow radiographs using a deep neural network model.European radiology · 2021Article
- DICOM Image ANalysis and Archive (DIANA): an Open-Source System for Clinical AI Applications.Journal of digital imaging · 2021Article
- Deep Learning for Pulmonary Embolism Detection: Tackling the RSNA 2020 AI Challenge.Radiology. Artificial intelligence · 2021Article
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Authors and funding
5 authors at 3 institutions in 1 country.
Funding
Abstract
purposeTo investigate improvements in performance for automatic bone age estimation that can be gained through model ensembling. MATERIALS AND
methodsA total of 48 submissions from the 2017 RSNA Pediatric Bone Age Machine Learning Challenge were used. Participants were provided with 12 611 pediatric hand radiographs with bone ages determined by a pediatric radiologist to develop models for bone age determination. The final results were determined using a test set of 200 radiographs labeled with the weighted average of six ratings. The mean pairwise model correlation and performance of all possible model combinations for ensembles of up to 10 models using the mean absolute deviation (MAD) were evaluated. A bootstrap analysis using the 200 test radiographs was conducted to estimate the true generalization MAD.
resultsThe estimated generalization MAD of a single model was 4.55 months. The best-performing ensemble consisted of four models with an MAD of 3.79 months. The mean pairwise correlation of models within this ensemble was 0.47. In comparison, the lowest achievable MAD by combining the highest-ranking models based on individual scores was 3.93 months using eight models with a mean pairwise model correlation of 0.67.
conclusionCombining less-correlated, high-performing models resulted in better performance than naively combining the top-performing models. Machine learning competitions within radiology should be encouraged to spur development of heterogeneous models whose predictions can be combined to achieve optimal performance.© RSNA, 2019
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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.