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Decentralized convolutional neural network for evaluating spinal deformity with spinopelvic parameters.

Dong-Sik Chae1, Thong Phi Nguyen2, Sung-Jun Park3

  • 1Department of Orthopaedic Surgery, International St. Mary's Hospital, Catholic Kwandong University College of Medicine, Incheon, Republic of Korea.

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Summary
This summary is machine-generated.

This study introduces an automated method using a decentralized convolutional neural network to accurately measure adult spinal deformity. This AI approach offers a faster, more efficient alternative to manual measurements for diagnosing low back pain.

Keywords:
Artificial intelligentConvolutional neural networkOrthopaedicRadiologySpinopelvic

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Area of Science:

  • Orthopedics and Rehabilitation
  • Medical Imaging and Artificial Intelligence
  • Spinal Biomechanics

Background:

  • Low back pain is a prevalent musculoskeletal condition often linked to abnormal spinal alignment.
  • Accurate assessment of adult spinal deformity is crucial for timely diagnosis and effective therapeutic planning.
  • Current manual measurement methods are time-consuming and struggle with large datasets.

Purpose of the Study:

  • To develop and validate an automated method for precise measurement of spinopelvic parameters.
  • To replace the labor-intensive manual process with an efficient deep learning approach.
  • To improve the analysis of adult spinal deformities in the era of big data.

Main Methods:

  • A decentralized convolutional neural network was employed for automated spinopelvic parameter measurement.
  • The method utilizes a region-of-interest (ROI) narrowing strategy for focused feature extraction.
  • Keypoints representing geometric characteristics were identified to calculate spinal deformity parameters.

Main Results:

  • The automated method demonstrated consistency with manual measurements in 40 test cases.
  • Mean absolute deviations ranged from 1.45° for Pelvic Tilt Angle (PTA) to 3.51° for Lumbar Lordosis Angle (LSA).
  • The approach offers a viable, data-driven solution for analyzing spinal alignment.

Conclusions:

  • The proposed automated method accurately measures spinopelvic parameters for assessing spinal deformity.
  • This AI-driven technique enhances efficiency and precision compared to traditional manual assessments.
  • The findings support the use of automated analysis for managing low back pain and related conditions.