Mateo Tomas Fariña Nuñez, Sven Theiler, Inka Berglar, Massimo Barbagallo, Massimo Bottini, Victor Gabriel El-Hajj, Stefanos Voglis, Nicolai Maldaner, Tamas F. Fekete, Daniel Haschtmann, Markus Loibl, Dezsö J. Jeszenszky, Maria L. Gandía-González, Menno R. Germans, David Bellut, Carlo Serra, Luca Regli, Erik Edström, Adrian Elmi-Terander, Victor E. Staartjes
Neurospine 2026;23(3):703-719. Published online July 31, 2026
Instability of the craniocervical junction is a potentially life-threatening condition requiring surgical stabilization. Traditional occipital plate fixation carries risks of construct loosening and intracranial complications due to variable skull thickness, particularly after posterior fossa decompression where plate fixation is challenging. Occipital condyle screws (OCS) provide direct fixation into the occipital condyles (OCs). However, comprehensive outcome data remains sparse. This systematic review and meta-analysis evaluated anatomical parameters, technical aspects, and surgical outcomes of OCS fixation in craniocervical stabilization. Following PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-analyses) guidelines, PubMed/MEDLINE, Embase, and Scopus were searched for studies reporting techniques and outcomes of occipitocervical fixation using OCS. Two reviewers independently extracted data, and study quality was assessed using the Newcastle-Ottawa Scale, when possible. Random-effects meta-analysis was performed. The primary endpoint was to characterize the technical aspects of craniocervical fixation using OCS and to ascertain its overall feasibility, defined by morphometric suitability, technical success rates, and complication rates. Thirty studies met inclusion: 12 cadaveric (618 specimens), 10 imaging (1,604 participants), and 8 surgical (284 patients). Morphometry consistently showed larger OC in male populations. Bicortical screw placement achieved 100% technical success. Standard 3.5-mm screws (18–24 mm) were commonly used. Recommended trajectories varied (sagittal with 18°–28° angulation; axial with 22°–37° angulation). No major symptomatic vascular or permanent neurological complications occurred. Meta-analytic data revealed significant differences in morphometric measurements of the OC and differences in the OCS length and angulation parameters. OCS fixation appears to be an anatomically feasible and technically promising fixation strategy in selected patients when anatomy and technique are carefully evaluated. Population-specific morphometric variability mandates individualized preoperative assessment. Future comparative studies should define long-term outcomes, fusion rates, and optimize region-specific surgical parameters.
This paper analyzes the regulatory frameworks for artificial intelligence/machine learning AI/ML-enabled medical devices in the European Union (EU), the United States (US), and the Republic of Korea, with a focus on applications in spine surgery. The aim is to provide guidance for developers and researchers navigating regulatory pathways. A review of current literature, regulatory documents, and legislative frameworks was conducted. Key differences in regulatory bodies, risk classification, submission requirements, and approval pathways for AI/ML medical devices were examined in the EU, US, and Republic of Korea. The EU AI Act (2024) establishes a risk-based framework, requiring regulatory review based on device risk, with high-risk devices subject to stricter oversight. The US applies a more flexible approach, allowing multiple submission pathways and incorporating a focus on continuous learning. The Republic of Korea emphasizes possibilities of streamlined approval and with growing use of real-world data to support validation. Developers must ensure regulatory alignment early in the development process, focusing on key aspects like dataset quality, transparency, and continuous monitoring. Across all regions, the need for technical documentation, quality management systems, and bias mitigation are essential for approval. Developers are encouraged to adopt adaptable strategies to comply with evolving regulatory standards, ensuring models remain transparent, fair, and reliable. The EU’s comprehensive AI Act enforces stricter oversight, while the US and Korea offer more flexible pathways. Developers of spine surgery AI/ML devices must tailor development strategies to align with regional regulations, emphasizing transparent development, quality assurance, and postmarket monitoring to ensure approval success.
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Objective Virtual and augmented reality have enjoyed increased attention in spine surgery. Preoperative planning, pedicle screw placement, and surgical training are among the most studied use cases. Identifying osseous structures is a key aspect of navigating a 3-dimensional virtual reconstruction. To automate the otherwise time-consuming process of labeling vertebrae on each slice individually, we propose a fully automated pipeline that automates segmentation on computed tomography (CT) and which can form the basis for further virtual or augmented reality application and radiomic analysis.
Methods Based on a large public dataset of annotated vertebral CT scans, we first trained a YOLOv8m (You-Only-Look-Once algorithm, Version 8 and size medium) to detect each vertebra individually. On the then cropped images, a 2D-U-Net was developed and externally validated on 2 different public datasets.
Results Two hundred fourteen CT scans (cervical, thoracic, or lumbar spine) were used for model training, and 40 scans were used for external validation. Vertebra recognition achieved a mAP50 (mean average precision with Jaccard threshold of 0.5) of over 0.84, and the segmentation algorithm attained a mean Dice score of 0.75 ± 0.14 at internal, 0.77 ± 0.12 and 0.82 ± 0.14 at external validation, respectively.
Conclusion We propose a 2-stage approach consisting of single vertebra labeling by an object detection algorithm followed by semantic segmentation. In our externally validated pilot study, we demonstrate robust performance for our object detection network in identifying individual vertebrae, as well as for our segmentation model in precisely delineating the bony structures.
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Objective Computed tomography (CT) imaging is a cornerstone in the assessment of patients with spinal trauma and in the planning of spinal interventions. However, CT studies are associated with logistical problems, acquisition costs, and radiation exposure. In this proof-of-concept study, the feasibility of generating synthetic spinal CT images using biplanar radiographs was explored. This could expand the potential applications of x-ray machines pre-, post-, and even intraoperatively.
Methods A cohort of 209 patients who underwent spinal CT imaging from the VerSe2020 dataset was used to train the algorithm. The model was subsequently evaluated using an internal and external validation set containing 55 from the VerSe2020 dataset and a subset of 56 images from the CTSpine1K dataset, respectively. Digitally reconstructed radiographs served as input for training and evaluation of the 2-dimensional (2D)-to-3-dimentional (3D) generative adversarial model. Model performance was assessed using peak signal to noise ratio (PSNR), structural similarity index (SSIM), and cosine similarity (CS).
Results At external validation, the developed model achieved a PSNR of 21.139 ± 1.018 dB (mean ± standard deviation). The SSIM and CS amounted to 0.947 ± 0.010 and 0.671 ± 0.691, respectively.
Conclusion Generating an artificial 3D output from 2D imaging is challenging, especially for spinal imaging, where x-rays are known to deliver insufficient information frequently. Although the synthetic CT scans derived from our model do not perfectly match their ground truth CT, our proof-of-concept study warrants further exploration of the potential of this technology.
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Martin Nikolaus Stienen, Nicolai Maldaner, Marketa Sosnova, Holger Joswig, Marco Vincenzo Corniola, Luca Regli, Gerhard Hildebrandt, Karl Schaller, Oliver Pascal Gautschi
Neurospine 2020;17(1):270-280. Published online January 26, 2020
Objective The patient-reported outcome measure (PROM)-based evaluation in lumbar degenerative disc disease (DDD) is today’s gold standard but has limitations. We studied the impact of lower extremity motor deficits (LEMDs) on PROMs and a new objective outcome measure.
Methods We evaluated patients with lumbar DDD from a prospective 2-center database. LEMDs were graded according to the British Medical Research Council (BMRC; 5 [normal] –0 [no movement]). The PROM-based evaluation included pain (visual analogue scale), disability (Oswestry Disability Index [ODI] & Roland-Morris Disability Index [RMDI]), and health-related quality of life (HRQoL; Short-Form 12 physical component summary/mental component summary & EuroQol-5D index). Objective functional impairment (OFI) was determined as age- and sex-adjusted Timed-Up and Go (TUG) test value.
Results One hundred five of 375 patients (28.0%) had a LEMD. Patients with LEMD had slightly higher disability (ODI: 52.8 vs. 48.2, p = 0.025; RMDI: 12.6 vs. 11.3, p = 0.034) but similar pain and HRQoL scores. OFI T-scores were significantly higher in patients with LEMD (144.2 vs. 124.3, p = 0.006). When comparing patients with high- (BMRC 0–2) vs. low-grade LEMD (BMRC 3–4), no difference was evident for the PROM-based evaluation (all p > 0.05) but patients with high-grade LEMD had markedly higher OFI T-scores (280.9 vs. 136.0, p = 0.001). Patients with LEMD had longer TUG test times and OFI T-scores than matched controls without LEMDs.
Conclusion Our data suggest that PROMs fail to sufficiently account for LEMD-associated disability, which is common and oftentimes bothersome to patients. The objective functional evaluation with the TUG test appears to be more sensitive to LEMD-associated disability. An objective functional evaluation of patients with LEMD appears reasonable.
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