RESUMEN
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.
RESUMEN
Background: The delay in the referral of patients with potential surgical vertebral metastasis (VM) to the spine surgeon is strongly associated with a worse outcome. The spinal instability neoplastic score (SINS) allows for determining the risk of instability of a spine segment with VM; however, it is almost exclusively used by specialists or residents in neurosurgery or orthopedics. The objective of this work is to report the delay in surgical consultation of patients with potentially unstable and unstable VM (SINS >6) at our center. Material: We performed a 5-year single-center retrospective analysis of patients with spine metastasis on computed tomography (CT). Patients were divided into Group 1 (G1), potentially unstable VM (SINS 7-12), and Group 2 (G2), unstable VM (SINS 13-18). Time to surgical referral was calculated as the number of days between the report of the VM in the CT and the first clinical assessment of a spinal surgeon on the medical records. Results: We analyzed 220 CT scans, and 98 met the selection criteria. Group 1 had 85 patients (86.7%) and Group 2 had 13 (13.3%). We observed a mean time to referral of 83.5 days in the entire cohort (std = 127.6); 87.6 days (std = 135.1) for G1, and 57.2 days (std = 53.8) for G2. The delay in referral showed no significant correlation with the SINS score. Conclusion: We report a mean delay of 83.5 days in the surgical referral of VM (SINS >6, n = 98). Both groups showed cases of serious referral delay, with 25% of patients having the first surgical consultation more than three months after the CT study.
