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.
Artificial intelligence in spine surgery: a scoping review Anis Choucha, Morgane Evin, Matteo de Simone, Guillaume Dannhoff, Henry Dufour, Valentin Avinens, Kaissar Farah, Florian Saby, Stephane Fuentes Neurochirurgie.2026; 72(1): 101764. CrossRef
Deep Learning-Based Projection Angle Estimation for Lumbar Oblique Radiography: A Two-Stage Object Detection Approach Using Vertebral–Pedicle Ratio Analysis Riria Yamamoto, Kaori Tsutsumi, Takaaki Yoshimura, Hiroyuki Sugimori Applied Sciences.2026; 16(6): 2800. CrossRef
AI applications in lumbar and lumbosacral pedicle screw placement: a systematic review of limited evidence and future directions Pakpoom Thintharua, Ratchaphon Prabrai, Anuyut khamsiriwatchara, Rohan Sethi, Sorayouth Chumnanvej Neurosurgical Review.2026;[Epub] CrossRef
Vertebra-Level Completeness Analysis in Thoracolumbar Ultrasound Using a YOLO-Based Detection Framework Sumartini Dana, Chen Zhang, Yongping Zheng, Sai Ho Ling Sensors.2026; 26(7): 2101. CrossRef
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Dual-cooperation of spatial and local cues enhances point-supervised spinal structure segmentation Pei Wang, Jiansong Fan, Xiang Pan, Wei Chu Applied Soft Computing.2026; 202: 115993. CrossRef
The Application of Artificial Intelligence in Spine Surgery: A Scoping Review Liangyu Shi, Hongfei Wang, Graham Ka-Hon Shea JAAOS: Global Research and Reviews.2025;[Epub] CrossRef
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Mask prompt-guided multi-stage network for vertebrae identification Zhikai Zhou, Ziyue Zhang, Mingying Li, Lei Song, Youyong Kong, Jean Louis Coatrieux, Huazhong Shu Biomedical Signal Processing and Control.2025; 110: 108091. CrossRef
Empowering the future of spinal surgery through digital and intelligent technologies Chenfei Gao, Tianyu Yao, Tenghui Zhang, Wenyu Zhang, Jianxi Wang, Fazhi Zang, Huajiang Chen Spine Research.2025; 1(1): 23. CrossRef
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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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