ArticleJournal of craniovertebral junction & spine
Autonomous image segmentation and identification of anatomical landmarks from lumbar spine intraoperative computed tomography scans using machine learning: A validation study.
Article in Journal of craniovertebral junction & spine. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers, 1 of them a synthesis that pooled it.
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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.
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Who cites it
12 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Artificial Intelligence and Computer Vision in Low Back Pain: A Systematic Review.International journal of environmental research and public health · 2021Pooled it
- Abd-El-Barr's Mountain-A Radiographic Landmark for Consistent and Successful Docking in Minimally Invasive Lumbar Surgery: A Cadaveric Study.International journal of spine surgery · 2026Article
- Mixed reality combined with surgical navigation versus conventional navigation for resection of oral and maxillofacial tumors: a comparative study.BMC oral health · 2026Article
- Artificial Intelligence in Spine Surgery: Imaging-Based Applications for Diagnosis and Surgical Techniques.Current reviews in musculoskeletal medicine · 2025Review
- Comprehensive Approaches to Pain Management in Postoperative Spinal Surgery Patients: Advanced Strategies and Future Directions.Neurology international · 2025Review
- Artificial Intelligence in Surgery: A Systematic Review of Use and Validation.Journal of clinical medicine · 2024Review
- Artificial Intelligence in Spinal Imaging and Patient Care: A Review of Recent Advances.Neurospine · 2024Article
- Article
- Current Trends in Artificial Intelligence-Assisted Spine Surgery: A Systematic Review.Asian spine journal · 2024Article
- Current Applications of Machine Learning in Spine: From Clinical View.Global spine journal · 2022Review
- Augmented Reality in Spine Surgery: Current State of the Art.International journal of spine surgery · 2022Article
- Autonomous lumbar spine pedicle screw planning using machine learning: A validation study.Journal of craniovertebral junction & spineArticle
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
purposeMachine-learning algorithms are a subset of artificial intelligence that have proven to enhance analytics in medicine across various platforms. Spine surgery has the potential to benefit from improved hardware placement utilizing algorithms that autonomously and accurately measure pedicle and vertebral body anatomy. The purpose of this study was to assess the accuracy of an autonomous convolutional neural network (CNN) in measuring vertebral body anatomy utilizing clinical lumbar computed tomography (CT) scans and automatically segment vertebral body anatomy.
methodsThe CNN was trained utilizing 8000 manually segmented CT slices from 15 cadaveric specimens and 30 adult diagnostic scans. Validation was performed with twenty randomly selected patient datasets. Anatomic landmarks that were segmented included the pedicle, vertebral body, spinous process, transverse process, facet joint, and lamina. Morphometric measurement of the vertebral body was compared between manual measurements and automatic measurements.
resultsAutomatic segmentation was found to have a mean accuracy ranging from 96.38% to 98.96%. Coaxial distance from the lamina to the anterior cortex was 99.10% with pedicle angulation error of 3.47%.
conclusionThe CNN algorithm tested in this study provides an accurate means to automatically identify the vertebral body anatomy and provide measurements for implants and placement trajectories.
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