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Automated Cobb Angle Measurement for Adolescent Idiopathic Scoliosis Using Convolutional Neural Network.

Wahyu Caesarendra1, Wahyu Rahmaniar2, John Mathew3

  • 1Manufacturing Systems Engineering, Faculty of Integrated Technologies, Universiti Brunei Darussalam, Jalan Tungku Link, Gadong BE1410, Brunei.

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Summary

This study introduces a deep learning model for automatic Cobb angle measurement in scoliosis patients. The AI tool accurately detects spine vertebrae on X-rays, improving measurement reliability in clinical settings.

Keywords:
convolutional neural network (CNN)deep learningscoliosisspine classificationvertebrae

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Orthopedics

Background:

  • Cobb angle measurement for scoliosis is subject to significant observer variability.
  • Accurate and reliable Cobb angle assessment is crucial for diagnosing and managing scoliosis.

Purpose of the Study:

  • To develop and validate a deep learning architecture for automated Cobb angle measurement from X-ray images.
  • To reduce inter- and intra-observer variations in clinical Cobb angle assessments.

Main Methods:

  • A convolutional neural network architecture was designed to detect 68 spine landmark features and 17 vertebrae from anterior-posterior X-ray images.
  • The model was trained and tested using the AASCE MICCAI 2019 dataset and local patient images.
  • Vertebrae locations were processed to automatically calculate the Cobb angle.

Main Results:

  • The proposed deep learning method achieved up to 93.6% accuracy in Cobb angle measurement.
  • The system demonstrated excellent reliability, with an intraclass correlation coefficient greater than 0.95 compared to clinician measurements.

Conclusions:

  • The developed deep learning architecture offers a reliable and accurate tool for augmenting Cobb angle measurement in adolescent idiopathic scoliosis.
  • This AI-driven approach has the potential to enhance clinical decision-making and patient care in real-world settings.