ArticleComputational and mathematical methods in medicine2013
Automated bone age assessment: motivation, taxonomies, and challenges.
Article in Computational and mathematical methods in medicine, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers, 1 of them a synthesis that pooled it.
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Who cites it
19 citing papers in PubMed, 1 synthesis or guideline pooled it, 54 citations in OpenAlex.
- Bone age assessment with various machine learning techniques: A systematic literature review and meta-analysis.PloS one · 2019Pooled it
- A comparative study of the interpretation results of different artificial intelligence bone age assessment software.Journal of pediatric endocrinology & metabolism : JPEM · 2026Article
- Performance of two different artificial intelligence (AI) methods for assessing carpal bone age compared to the standard Greulich and Pyle method.La Radiologia medica · 2024Article
- Axial Attention Convolutional Neural Network for Brain Tumor Segmentation with Multi-Modality MRI Scans.Brain sciences · 2022Article
- External validation of deep learning-based bone-age software: a preliminary study with real world data.Scientific reports · 2022Article
- Automated Bone Age Assessment Using Artificial Intelligence: The Future of Bone Age Assessment.Korean journal of radiology · 2021Review
- Bone age assessment based on deep convolution neural network incorporated with segmentation.International journal of computer assisted radiology and surgery · 2020Article
- Chronological Age Assessment in Young Individuals Using Bone Age Assessment Staging and Nonradiological Aspects: Machine Learning Multifactorial Approach.JMIR medical informatics · 2020Article
- Age Assessment of Youth and Young Adults Using Magnetic Resonance Imaging of the Knee: A Deep Learning Approach.JMIR medical informatics · 2019Article
- Bone age determination in eutrophic, overweight and obese Brazilian children and adolescents: a comparison between computerized BoneXpert and Greulich-Pyle methods.Pediatric radiology · 2019Article
- Forensic age estimation for pelvic X-ray images using deep learning.European radiology · 2019Article
- A simple method for bone age assessment: the capitohamate planimetry.European radiology · 2018Article
- Ossification area localization in pediatric hand radiographs using deep neural networks for object detection.PloS one · 2018Article
- Validation of automated Greulich-Pyle bone age determination in children with chronic renal failure?Pediatric nephrology (Berlin, Germany) · 2015Article
- A Unified Framework for Brain Segmentation in MR Images.Computational and mathematical methods in medicine · 2015Article
- Article
- Bone Age in Endocrinology - Part 1: Methods of Bone Age Assessment.Indian journal of endocrinology and metabolismReview
- Cervical Vertebral Maturation Assessment using various Machine Learning techniques on Lateral cephalogram: A systematic literature review.Journal of oral biology and craniofacial researchReview
- A Comprehensive Review of Artificial Intelligence Methods in Bone Age Assessment.Healthcare technology lettersReview
Corrections and comments
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Authors and funding
6 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Bone age assessment (BAA) of unknown people is one of the most important topics in clinical procedure for evaluation of biological maturity of children. BAA is performed usually by comparing an X-ray of left hand wrist with an atlas of known sample bones. Recently, BAA has gained remarkable ground from academia and medicine. Manual methods of BAA are time-consuming and prone to observer variability. This is a motivation for developing automated methods of BAA. However, there is considerable research on the automated assessment, much of which are still in the experimental stage. This survey provides taxonomy of automated BAA approaches and discusses the challenges. Finally, we present suggestions for future research.
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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.