Pediatric Radiology: An Analysis of AI-Powered Bone Age Determination Methods
Significant progress has been made in using artificial intelligence, especially deep learning, to help doctors evaluate the bone age of children in medical images. Traditional methods like the Leather Tanner-Whitehouse and Greulich-Pyle approaches have some issues with consistency and accuracy. But...
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| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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Northern Technical University
2025-03-01
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| Series: | NTU Journal of Engineering and Technology |
| Online Access: | https://journals.ntu.edu.iq/index.php/NTU-JET/article/view/1030 |
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| Summary: | Significant progress has been made in using artificial intelligence, especially deep learning, to help doctors evaluate the bone age of children in medical images. Traditional methods like the Leather Tanner-Whitehouse and Greulich-Pyle approaches have some issues with consistency and accuracy. But with AI, there's been a big shift. This review looks at how AI has changed bone age evaluation over time, making it easier and more reliable. It covers different AI systems used, from older semi-automated ones like HANDX to newer ones like BoneXpert. The review explains how these systems work, their pros and cons, and how well they perform. It's a helpful guide for scientists, doctors, and anyone interested in this field, covering both old and new AI-driven methods for evaluating bone age.
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| ISSN: | 2788-9971 2788-998X |