Autonomous extraction of preoperative radiographic predictors on X-ray for cervical spine deformity following laminoplasty: a prospectively validated AI tool
Year: 2025
Type: article
Abstract: INTRODUCTION: Approximately 21% of patients who undergo cervical laminoplasty for cervical spondylotic myelopathy (CSM) develop postoperative kyphotic deformity (KD). Radiologic parameters (RPs) on preoperative sagittal X-ray have consistently shown to be the strongest predictors for KD but their acquisition requires manual labor from specialists. Thus, the authors developed a novel artificial intelligence (AI) model to autonomously retrieve the predictors. MATERIAL AND METHODS: A total of 259 patients' sagittal X-rays were retrospectively obtained from the internal center between 2016 and 2024 for training. Patients with spinal deformities, prior surgical interventions, or tumors/malignancies within the cervical region were excluded. Data augmentation techniques were used to amplify the dataset, and a custom attention U-net architecture was used. Prospective enrollment of patients diagnosed with CSM over nine months at the internal and external center was performed for validation. RESULTS: A total of 28 (77.8%) patients with CSM were prospectively obtained. The mean age was 66.0 ± 10.5 years, and 20 (71.4%) were females. The mean duration for human extraction of all RPs among the 2 neurosurgeons was 116.2 ± 17.5 seconds, whereas AI performed the task in 0.7 ± 0.0 seconds (p < 0.001). No significant difference was observed between the AI-obtained RPs and the physicians' report RP values, except for the center of gravity of the head to the C7 sagittal vertical axis, which reported a significant mean difference (p = 0.049); however, the extent of the difference was minimal (2.3 ± 4.0 mm). CONCLUSIONS: This study automated the extraction of RPs for postoperative KD following laminoplasty. The trained model is publicly available for software developers to implement on a standalone platform or as a plugin on a medical imaging viewer. The model thus incentivizes the development of a risk scoring system for KD that utilizes the AI-acquired RPs to improve the evidence-based practice when selecting the surgical approach.
DOI: 10.5603/pjnns.105002
Authors: Samuel D. Pettersson, Natalia A. Koc, Paulina Skrzypkowska, Jean Filo, Kamil Siedlecki, Katarzyna M. Aleksandrowicz, Joon Lee, Omar Alwakaa, Redwan Jabbar, Fernando Terry, Miran Faraj, Tomasz Szmuda, Piotr Zieliński, Ziev B. Moses, Leszek Sagan, Tomasz Klepinowski
Language: English
Subfield: Surgery
Field: Medicine
Domain: Health Sciences
Open Access status: hybrid
OpenAlex ID: W4415812242
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