Fully Automated Deep Learning System for Bone Age Assessment
Open accessAbstract
Skeletal maturity progresses through discrete phases, a fact that is used routinely in pediatrics where bone age assessments (BAAs) are compared to chronological age in the evaluation of endocrine and metabolic disorders. While central to many disease evaluations, little has changed to improve the tedious process since its introduction in 1950. In this study, we propose a fully automated deep learning pipeline to segment a region of interest, standardize and preprocess input radiographs, and perform BAA. Our models use an ImageNet pretrained, fine-tuned convolutional neural network (CNN) to achieve 57.32 and 61.40% accuracies for the female and male cohorts on our held-out test images. Female test radiographs were assigned a BAA within 1 year 90.39% and within 2 years 98.11% of the time. Male test radiographs were assigned 94.18% within 1 year and 99.00% within 2 years. Using the input occlusion method, attention maps were created which reveal what features the trained model uses to perform BAA. These correspond to what human experts look at when manually performing BAA. Finally, the fully automated BAA system was deployed in the clinical environment as a decision supporting system for more accurate and efficient BAAs at much faster interpretation time (<2 s) than the conventional method.
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BoneXpert is one of the first commercial applications of AI in radiology and appeared to be the only AI application in clinical use across several hospitals in The Netherlands.
“BoneXpert is one of the first commercial applications of AI in radiology [25] and appeared to be the only AI application in clinical use across several hospitals in The Netherlands.”
From Implementation of artificial intelligence (AI) applications in radiology: hindering and facilitating factors (2020) · cites this paper as [25]
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- Authors
- Lee, H., Tajmir, S., Lee, J., Zissen, M., Yeshiwas, B. A., Alkasab, T. K., Choy, G., & Do, S.
- Affiliations
- Harvard University (US)
- Corresponding author
- Do, Synho
- Venue
- Journal of Digital Imaging
- Published
- 2017-03-08
- Volume
- 30
- Issue
- 4
- Pages
- 427-441
- ISSN
- 0897-1889 (ISSN-L)
- 1618-727X
- PMC
- PMC5537090
- OpenAlex
- W2592765733
- Cited by
- 452
- References
- 40
- Citations by year
- 2026: 21 · 2025: 37 · 2024: 45 · 2023: 57 · 2022: 55 · 2021: 62 · 2020: 75 · 2019: 47 · 2018: 45 · 2017: 8
- FWCI
- 276.78
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Locations
- Journal of Imaging Informatics in Medicine (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Digital Access to Scholarship at Harvard (DASH) (Harvard University) (submittedVersion, cc-by)
- Topics
- Social Sciences › Arts and Humanities › Archeology › Forensic Anthropology and Bioarchaeology Studies
- Physical Sciences › Engineering › Biomedical Engineering › Medical Imaging and Analysis
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Keywords
- bone age assessment, skeletal maturity, convolutional neural networks, attention maps, decision support system, pediatric endocrine disorders, automated image analysis
- MeSH terms
- Machine Learning, Adolescent, Adult, Age Determination by Skeleton, Child, Female, Hand, Humans, Male, Software, Neural Networks, Computer, Decision Support Systems, Clinical
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