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: Rayyan Mahmood Salih Alrawi, Nasseer M. Basheer
Format: Article
Language:English
Published: Northern Technical University 2025-03-01
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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author Rayyan Mahmood Salih Alrawi
Nasseer M. Basheer
author_facet Rayyan Mahmood Salih Alrawi
Nasseer M. Basheer
author_sort Rayyan Mahmood Salih Alrawi
collection DOAJ
description 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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series NTU Journal of Engineering and Technology
spelling doaj-art-81a0befd28254c75992bd9b2595fa6af2025-08-24T13:19:20ZengNorthern Technical UniversityNTU Journal of Engineering and Technology2788-99712788-998X2025-03-014110.56286/a82tjh481031Pediatric Radiology: An Analysis of AI-Powered Bone Age Determination MethodsRayyan Mahmood Salih Alrawi0Nasseer M. Basheer1https://orcid.org/0000-0002-0675-6971Northern Technical UniversityNorthern Technical University 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. https://journals.ntu.edu.iq/index.php/NTU-JET/article/view/1030
spellingShingle Rayyan Mahmood Salih Alrawi
Nasseer M. Basheer
Pediatric Radiology: An Analysis of AI-Powered Bone Age Determination Methods
NTU Journal of Engineering and Technology
title Pediatric Radiology: An Analysis of AI-Powered Bone Age Determination Methods
title_full Pediatric Radiology: An Analysis of AI-Powered Bone Age Determination Methods
title_fullStr Pediatric Radiology: An Analysis of AI-Powered Bone Age Determination Methods
title_full_unstemmed Pediatric Radiology: An Analysis of AI-Powered Bone Age Determination Methods
title_short Pediatric Radiology: An Analysis of AI-Powered Bone Age Determination Methods
title_sort pediatric radiology an analysis of ai powered bone age determination methods
url https://journals.ntu.edu.iq/index.php/NTU-JET/article/view/1030
work_keys_str_mv AT rayyanmahmoodsalihalrawi pediatricradiologyananalysisofaipoweredboneagedeterminationmethods
AT nasseermbasheer pediatricradiologyananalysisofaipoweredboneagedeterminationmethods