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Skeletal age evaluation using hand X-rays to determine growth problems.

Muhammad Umer1, Ala' Abdulmajid Eshmawi2, Khaled Alnowaiser3

  • 1Department of Computer Science & Information Technology, The Islamia University of Bahawalpur, Bahawalpur, Pakistan.

Peerj. Computer Science
|December 11, 2023
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Summary
This summary is machine-generated.

Accurate skeletal age estimation using X-ray images is crucial for diagnosing pediatric growth disorders. A novel customized convolutional neural network (CNN) achieved 97% accuracy in assessing bone age, outperforming existing models.

Keywords:
Bone disorder detectionData augmentationMachine learningSkeletal age estimation

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Area of Science:

  • Pediatric Radiology
  • Medical Imaging Analysis
  • Artificial Intelligence in Healthcare

Background:

  • Skeletal age estimation from X-rays is vital for identifying infant and newborn bone growth anomalies.
  • Bone abnormalities can stem from various conditions, impacting growth plates and potentially causing permanent joint damage.
  • Discrepancies between chronological and skeletal age signal potential growth problems, necessitating accurate assessment for early diagnosis.

Purpose of the Study:

  • To develop an automated system for accurate skeletal age estimation using pediatric hand X-rays.
  • To evaluate the efficacy of a customized convolutional neural network (CNN) for detecting hand bone maturation.
  • To compare the performance of the proposed CNN model against the Visual Geometry Group (VGG) model.

Main Methods:

  • Utilized the Radiological Society of North America's Pediatric Bone Age Challenge dataset (12,600 images).
  • Developed a customized convolutional neural network (CNN) model for bone age assessment.
  • Employed data augmentation techniques to enhance dataset size and model training.

Main Results:

  • The customized CNN model achieved 97% accuracy in skeletal age estimation.
  • The proposed CNN model demonstrated superior performance compared to the VGG model.
  • Data augmentation techniques positively impacted the model's training and accuracy.

Conclusions:

  • The developed customized CNN model offers a highly accurate and efficient method for skeletal age estimation.
  • This AI-driven approach can aid clinicians in diagnosing growth anomalies and endocrine disorders in children.
  • The findings highlight the potential of deep learning in advancing pediatric radiological assessments.