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Using artificial intelligence models to evaluate envisaged points initially: A pilot study.

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Summary

This study validates anatomical landmarks on hand-wrist radiographs (HWRs) for skeletal maturity assessment using neural networks. The developed models show promising classification performance, supporting their use in future research.

Keywords:
Artificial intelligenceage determination by skeletonhand-wristmachine learningradiology

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Area of Science:

  • Radiology
  • Biomedical Engineering
  • Orthopedics

Background:

  • Skeletal maturity assessment is crucial in clinical practice.
  • Hand-wrist radiographs (HWRs) offer valuable indicators of skeletal development.
  • Standardized classification of phalangeal morphology is needed.

Purpose of the Study:

  • To validate anatomical landmarks for classifying phalangeal bone morphology in HWRs.
  • To develop and evaluate classical neural network (NN) classifiers for skeletal maturity assessment.
  • To assess the reliability of epiphysis-diaphysis relationship classifications.

Main Methods:

  • A dataset of 136 HWRs was used to train classical neural network (NN) classifiers (NN-1 and NN-2 with 5-fold cross-validation).
  • 22 anatomical landmarks were identified on phalanges (PP3, MP3, DP3, MP5) and epiphysis-diaphysis relationships were categorized.
  • 18 ratios and 15 angles were extracted, and model performance was evaluated using agreement, Kappa coefficients, precision, recall, F1-score, and accuracy.

Main Results:

  • Method error ranged from Cohen's Kappa (cκ) of 0.7-1.
  • Overall classification performance ranged from 82.14% to 89.29%.
  • Average performance for NN-1 and NN-2 models was 85.71% and 85.52%, respectively, with promising results except in regions with limited samples.

Conclusions:

  • The validated anatomical landmarks are suitable for future skeletal maturity studies.
  • Neural network models demonstrate effective classification of phalangeal morphology.
  • This approach offers a reliable method for radiological skeletal maturity assessment.