Artificial Intelligence Assistance for the Measurement of Full Alignment Parameters in Whole-Spine Lateral Radiographs

World Neurosurg. 2024 Jul:187:e363-e382. doi: 10.1016/j.wneu.2024.04.091. Epub 2024 Apr 20.

Abstract

Background: Measuring spinal alignment with radiological parameters is essential in patients with spinal conditions likely to be treated surgically. These evaluations are not usually included in the radiological report. As a result, spinal surgeons commonly perform the measurement, which is time-consuming and subject to errors. We aim to develop a fully automated artificial intelligence (AI) tool to assist in measuring alignment parameters in whole-spine lateral radiograph (WSL X-rays).

Methods: We developed a tool called Vertebrai that automatically calculates the global spinal parameters (GSPs): Pelvic incidence, sacral slope, pelvic tilt, L1-L4 angle, L4-S1 lumbo-pelvic angle, T1 pelvic angle, sagittal vertical axis, cervical lordosis, C1-C2 lordosis, lumbar lordosis, mid-thoracic kyphosis, proximal thoracic kyphosis, global thoracic kyphosis, T1 slope, C2-C7 plummet, spino-sacral angle, C7 tilt, global tilt, spinopelvic tilt, and hip odontoid axis. We assessed human-AI interaction instead of AI performance alone. We compared the time to measure GSP and inter-rater agreement with and without AI assistance. Two institutional datasets were created with 2267 multilabel images for classification and 784 WSL X-rays with reference standard landmark labeled by spinal surgeons.

Results: Vertebrai significantly reduced the measurement time comparing spine surgeons with AI assistance and the AI algorithm alone, without human intervention (3 minutes vs. 0.26 minutes; P < 0.05). Vertebrai achieved an average accuracy of 83% in detecting abnormal alignment values, with the sacral slope parameter exhibiting the lowest accuracy at 61.5% and spinopelvic tilt demonstrating the highest accuracy at 100%. Intraclass correlation analysis revealed a high level of correlation and consistency in the global alignment parameters.

Conclusions: Vertebrai's measurements can accurately detect alignment parameters, making it a promising tool for measuring GSP automatically.

Keywords: Artificial intelligence; Deep learning; Spinal parameters; Spine alignment.

MeSH terms

  • Adult
  • Artificial Intelligence*
  • Female
  • Humans
  • Lordosis / diagnostic imaging
  • Lordosis / surgery
  • Male
  • Middle Aged
  • Radiography / methods
  • Spine / diagnostic imaging
  • Spine / surgery