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Journals / Annals of Medical Research / 2021 / Cilt: 28 - Sayı: 7

Determination of bone age using deep convolutionalneural networks

Fatih Ahmet ŞENEL
Veysel Atilla AYYILDIZ
Ahmet DURSUN
Kenan ÖZTÜRK
Pages
1381–1386
Publication Date
2022-10-12
DOI
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

On average, the success rate was best at 98.39% with different training-testing split rates.Conclusion: This study demonstrated that deep learning could be used to determine bone age.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:Aim: Bone age assessment is an important measure of skeletal maturity in children with a growth development disorder.
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Key Findings & Data:In this study, bone age was determined from X-rays of the left wrist using convolutional neural networks, which are a popular subject of recent years.Results: In the study, in which a total of 150 patients' images were used, different deep learning architectures were used and the results were compared.
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Conclusion & Impact:On average, the success rate was best at 98.39% with different training-testing split rates.Conclusion: This study demonstrated that deep learning could be used to determine bone age.
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:Domain Parameters

Abstract

Aim: Bone age assessment is an important measure of skeletal maturity in children with a growth development disorder. Furthermore, age estimation is an important method applied in various situations such as growth observation, immigrant registration, legal criminal justice, and body detection. In this study, we aimed to develop a computer-assisted bone age detection system.Materials and Methods: This detection is usually evaluated by a trained physician using a radiological examination of the left wrist and a reference model. However, this evaluation method was stated to cause differences brought by interobserver and intraobserver variabilities. Several automated approaches have been proposed to overcome these problems, but none of them have been proven to be generalized according to different races, age ranges, and gender. Considering today's technology, it is observed that developments in the software are already used in the field of health. In this study, bone age was determined from X-rays of the left wrist using convolutional neural networks, which are a popular subject of recent years.Results: In the study, in which a total of 150 patients' images were used, different deep learning architectures were used and the results were compared. On average, the success rate was best at 98.39% with different training-testing split rates.Conclusion: This study demonstrated that deep learning could be used to determine bone age.

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Cite This Article

ŞENEL, F. A., AYYILDIZ, V. A., DURSUN, A., ÖZTÜRK, K. (2022). Determination of bone age using deep convolutionalneural networks. Annals of Medical Research, Cilt: 28 - Sayı: 7, 1381–1386. https://ajindex.com/articles/445957

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