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Automatic deep learning-based assessment of spinopelvic coronal and sagittal alignment.

Mohamed Zerouali1, Alexandre Parpaleix2, Mansour Benbakoura2

  • 1Department of Radiology, Institute for Locomotion, Sainte-Marguerite Hospital, APHM, 13009 Marseille, France.

Diagnostic and Interventional Imaging
|March 23, 2023
PubMed
Summary

An artificial intelligence (AI) solution accurately estimates spinopelvic alignment from spinal radiographs, showing high consistency with expert radiologists. This AI tool demonstrates potential for reducing radiologist workload in clinical practice.

Keywords:
Artificial intelligenceAutomated analysisDeep learningSpine deformitiesUniplanar whole-spine radiographs

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Area of Science:

  • Radiology
  • Medical Imaging
  • Artificial Intelligence in Healthcare

Background:

  • Spinopelvic alignment is crucial for assessing spinal health.
  • Accurate measurement of spinopelvic parameters can be time-consuming.
  • AI offers a potential solution for efficient and accurate radiological assessments.

Purpose of the Study:

  • To evaluate an artificial intelligence (AI) solution for estimating coronal and sagittal spinopelvic alignment.
  • To assess the AI's performance against expert measurements on conventional radiographs.

Main Methods:

  • Retrospective observational study of 100 patients.
  • AI measurements were compared to ground truth data established by radiologists.
  • Performance metrics included mean absolute errors (MAE) and intraclass correlation coefficients (ICCs).

Main Results:

  • AI demonstrated excellent consistency for most spinopelvic parameters (coronal ICCs ≥ 0.95, sagittal ICCs ≥ 0.85).
  • High accuracy was observed in classifying low Cobb angle (91%), severe scoliosis (99%), and frontal pelvic asymmetry (94%).
  • AI provided reliable measurements in 72% of patients, with notable exceptions in kyphosis estimation.

Conclusions:

  • The AI solution provides consistent spinopelvic alignment assessments comparable to senior radiologists.
  • AI shows potential for reducing workload in routine clinical implementation.
  • Further refinement may be needed for specific parameters like kyphosis.