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Automating Scoliosis Measurements in Radiographic Studies with Machine Learning: Comparing Artificial Intelligence

Audrey Y Ha1, Bao H Do1, Adam L Bartret1

  • 1Department of Radiology, Stanford University, 300 Pasteur Drive, Stanford, CA, 94305, USA.

Journal of Digital Imaging
|February 12, 2022
PubMed
Summary

An AI system can automatically measure spinal curvature in scoliosis patients, offering results comparable to human radiologists. This technology aims to improve the accuracy and efficiency of Cobb angle measurements in clinical settings.

Keywords:
Artificial intelligenceCobb angleConvolutional neural networkDeep learningScoliosisSpine

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Area of Science:

  • Radiology
  • Medical Imaging
  • Artificial Intelligence

Background:

  • Scoliosis affects 2-3% of the US population, characterized by abnormal lateral spinal curvature.
  • The Cobb angle is the standard but variable measurement for spinal curvature in scoliosis.
  • High interobserver and intraobserver variability in Cobb angle measurements necessitates improved methods.

Purpose of the Study:

  • To develop and validate an AI system for automatic quantitative evaluation of the Cobb angle.
  • To compare AI-generated Cobb angle measurements with human reports in a clinical setting.

Main Methods:

  • Retrospective collection of 2150 frontal view scoliosis radiographs from patients aged ≥16 years.
  • Segmentation of thoracic and lumbar vertebral bodies using bounding boxes to train a Faster R-CNN Resnet-101 object detection model.
  • Development of a controller algorithm to derive Cobb angle and endplate measurements from localized vertebral centroid coordinates.

Main Results:

  • A significant correlation (0.89, p < 0.001) was found between AI-derived and clinical report Cobb angle measurements.
  • The mean difference between AI and clinical report angle measurements was 7.34°, comparable to published literature.
  • The AI system demonstrated feasibility in automating level-by-level spinal angulation measurement.

Conclusions:

  • An AI system can automate Cobb angle measurement in scoliosis with performance comparable to radiologists.
  • This automated approach has the potential to enhance the consistency and efficiency of scoliosis assessment.
  • Further validation in clinical settings can support the integration of AI tools for scoliosis management.