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Related Concept Videos

Bone Disorders01:29

Bone Disorders

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Aging and its effect on bone remodeling is the most common cause of bone disorders. In young and healthy people, bone deposition and resorption happen at an equal rate to maintain optimal bone health.
Bone deposition is also affected by the levels of sex hormones like estrogen and testosterone that promote osteoblast activity and bone matrix synthesis. When the level of these hormones decreases due to aging, it causes a reduction in bone deposition. As a result, bone resorption by osteoclasts...
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Cortical Bone Assessment Using Ultrasonic Guided Waves: A Reproducibility Study in a Healthy Population
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Automatic bone age assessment: a Turkish population study.

Samet Öztürk1, Murat Yüce2, Gül Gizem Pamuk3

  • 1Esenler Obstetrics & Gynecology and Pediatrics Hospital, Clinic of Radiology, İstanbul, Türkiye.

Diagnostic and Interventional Radiology (Ankara, Turkey)
|March 17, 2025
PubMed
Summary
This summary is machine-generated.

This study developed an automated bone age assessment (BAA) model using deep learning for the Turkish population, showing promising accuracy and efficiency compared to traditional methods.

Keywords:
Bone age assessmentInceptionV3artificial intelligenceconvolutional neural networkdeep learning

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Area of Science:

  • Radiology
  • Artificial Intelligence
  • Pediatric Endocrinology

Background:

  • Traditional bone age assessment (BAA) methods like the Greulich and Pyle atlas have limitations due to population variations and observer subjectivity.
  • Automated BAA using deep learning offers potential for increased speed and consistency, but research across diverse populations is limited.

Purpose of the Study:

  • To evaluate the performance of deep learning algorithms for BAA in the Turkish population.
  • To enhance bone age models by investigating the influence of demographic factors and data heterogeneity.

Main Methods:

  • A modified InceptionV3 deep learning model was trained and validated using 2,730 hand radiographs from a Turkish cohort (Bağcılar Hospital) and combined with public datasets (RSNA, RHPE).
  • The model processed 500 × 500-pixel images, and the best performing model based on lowest mean absolute error (MAE) on the validation set was selected.
  • A total of 19,387 radiographs were analyzed, with a subset of 546 randomly split for internal testing.

Main Results:

  • The combined model achieved high accuracy, estimating bone age within 24 months for 94% of cases in the internal test set.
  • The mean absolute error (MAE) was 9.2 months overall, 7 months on the public test set, and 11.5 months on the Turkish (Bağcılar) internal test data.
  • The model trained solely on Turkish data had an MAE of 12.7 months, showing no significant difference compared to the combined model on the Turkish dataset (P > 0.05), while a public-only model performed significantly worse (MAE 16.5 months, P < 0.05).

Conclusions:

  • An automated BAA model incorporating the Turkish population was successfully developed using deep learning, addressing a gap in current research.
  • The model demonstrates effectiveness in clinical settings, offering a reliable and efficient alternative to traditional, time-consuming BAA methods.
  • Continued data accumulation and integration of diverse datasets can further refine model accuracy, improving clinical decision-making and patient care.