ReviewCureus2025
Computer Vision Applications in Spinal Orthopaedics: A Scoping Review of Imaging-Based Algorithms for Diagnosis, Measurement, and Surgical Planning.
Review in Cureus, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
1 citing paper in PubMed.
- Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Computer vision has advanced in spinal imaging, enabling automated interpretation of radiographs, CT, and MRI for diagnosis, surgical planning, and postoperative assessment. The spine's complex anatomy and high imaging volume make it a key area for algorithmic assistance. This scoping review maps current applications of computer vision in spinal orthopaedics and describes the clinical tasks, imaging modalities, and computational methods used in published studies. A systematic search of Ovid MEDLINE and Embase was performed from January 1995 to October 2025. Studies were included if they applied automated or semi-automated computer vision techniques to spinal imaging for diagnostic, morphometric, or surgical planning. Two reviewers screened and recorded data in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews (PRISMA-ScR) guidelines. Twenty studies met the inclusion criteria. CT (45%) and MRI (35%) were the dominant imaging modalities, followed by radiographs (15%) and ultrasound (5%). Deep learning methods were employed in 90% of studies, mainly convolutional architectures such as U-Net, ResNet, and YOLOv5. Segmentation and vertebral labeling were the most common tasks (60%), achieving Dice coefficients of 0.86-0.97 and accuracies of 90-98%. Fracture detection networks (25%) reached AUCs of 0.91-0.95, while morphometric measurement algorithms (15%) produced intraclass correlations of 0.93-0.98 compared with human analysis. Despite strong technical performance, only 20% of studies included external validation, and none conducted prospective testing. Computer vision has demonstrated strong performance in spinal image segmentation and fracture detection, particularly on CT and MRI. Nevertheless, clinical translation remains limited. Future research should prioritize multi-center datasets, real-world validation, and integration into surgical clinical practice to support preoperative and intraoperative care.
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What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.