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

• Published in: World neurosurgery Vol. 187; pp. e363 - e382
• Main Authors: Landriel, Federico, Franchi, Bruno Cruz, Mosquera, Candelaria, Lichtenberger, Fernando Padilla, Benitez, Sonia, Aineseder, Martina, Guiroy, Alfredo, Hem, Santiago
• Format: Journal Article
• Language: English
• Published: United States Elsevier Inc 01.07.2024
• Subjects: Adult; Artificial Intelligence; Deep learning; Female; Humans; Lordosis - diagnostic imaging; Lordosis - surgery; Male; Middle Aged; Radiography - methods; Spinal parameters; Spine - diagnostic imaging; Spine - surgery; Spine alignment; Spine alignment; LL; SS; PT; GSP; PTK; AI; DL; ICC; Spinal parameters; CL; GT; WSL X-rays; Deep learning; DSNT; T1S; HBR; PI; Artificial intelligence
• ISSN: 1878-8750; 1878-8769; 1878-8769
• DOI: 10.1016/j.wneu.2024.04.091

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