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Scanning Skeletal Remains for Bone Mineral Density in Forensic Contexts
07:56

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Published on: January 29, 2018

Automatic bone age assessment based on intelligent algorithms and comparison with TW3 method.

Jian Liu1, Jing Qi, Zhao Liu

  • 1Department of Pediatrics, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China.

Computerized Medical Imaging and Graphics : the Official Journal of the Computerized Medical Imaging Society
|October 7, 2008
PubMed
Summary
This summary is machine-generated.

New algorithms enhance automatic bone age assessment (ABAA) accuracy using particle swarm optimization (PSO) and artificial neural networks (ANNs). This intelligent system effectively analyzes hand-wrist radiographs for bone age determination in children aged 0-18 years.

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Area of Science:

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

Background:

  • Accurate bone age assessment (ABAA) is crucial for evaluating growth and development in children.
  • Traditional methods can be subjective and time-consuming.
  • Advancements in AI offer potential for more objective and efficient ABAA.

Purpose of the Study:

  • To develop and validate novel algorithms for improving the accuracy and practicality of automatic bone age assessment (ABAA).
  • To enhance the reliability of bone age determination using intelligent algorithms.
  • To apply these algorithms across the full pediatric age range (0-18 years).

Main Methods:

  • Object-based region of interest (ROI) segmentation for thirteen radius, ulna, and short finger (RUS) bones and seven carpal bones, following the Tanner-Whitehouse (TW3) method.
  • Feature extraction using particle swarm optimization (PSO), including size, morphology, and fusion stages.
  • Artificial neural network (ANN) classifiers trained with back-propagation for processing RUS and carpal features, utilizing 1046 hand-wrist radiographs.

Main Results:

  • High concordance rates between observers for both RUS (95.5%) and carpal (94.2%) bone age assessments, with no significant differences.
  • Excellent agreement between the automated system (ABAA) and manual TW3 readings for both RUS (97%) and carpal (93.8%) regions.
  • The proposed ABAA system demonstrated high accuracy and reliability across different age groups and observers.

Conclusions:

  • The PSO method significantly improved image segmentation and feature extraction validity and accuracy.
  • ANN models proved effective in processing complex image information for bone age assessment.
  • The intelligent algorithm-based ABAA system is successfully applicable for bone age determination in individuals from 0 to 18 years.