ArticleJournal of craniovertebral junction & spine
Autonomous lumbar spine pedicle screw planning 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 15 papers, 1 of them a synthesis that pooled it.
What it found
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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.
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Who cites it
15 citing papers in PubMed, 1 synthesis or guideline pooled it, 19 citations in OpenAlex.
- AI applications in lumbar and lumbosacral pedicle screw placement: a systematic review of limited evidence and future directions.Neurosurgical review · 2026Pooled it
- Agreement Between Automated Pedicle Screw Planning and Surgeon-Selected Implant Dimensions in Thoracic and Lumbar Spine Surgery: A Three-Year Single-Center Study.Journal of clinical medicine · 2026Article
- Exploring the potential of artificial intelligence and machine learning in orthopaedic surgery.Bulletin of the Hospital for Joint Disease (2013) · 2026Review
- Investigating robot control policy learning for autonomous x-ray-guided spine procedures.International journal of computer assisted radiology and surgery · 2026Article
- Exploring Artificial Intelligence in Orthopedic Surgery: A Review of Perception, Decision, and Execution Systems.Sensors (Basel, Switzerland) · 2026Review
- Feasibility of automatic screw planning via transformer-based shape completion from RGB-D imaging.Scientific reports · 2025Article
- Artificial Intelligence in Spine Surgery: Imaging-Based Applications for Diagnosis and Surgical Techniques.Current reviews in musculoskeletal medicine · 2025Review
- Can artificial intelligence in spine imaging affect current practice? Practical developments and their clinical status.North American Spine Society journal · 2025Article
- Wearable Devices in Scoliosis Treatment: A Scoping Review of Innovations and Challenges.Bioengineering (Basel, Switzerland) · 2025Review
- AI and machine learning in paediatric spine deformity surgery.Bone & joint open · 2025Article
- Autonomous Establishment of CT-Independent Sections to Obtain Optimum Pedicle-Screw Axis in Direction, Length, and Safety Margin.The international journal of medical robotics + computer assisted surgery : MRCAS · 2025Article
- A Network Meta-Analysis Comparing the Efficacy and Safety of Pedicle Screw Placement Techniques Using Intraoperative Conventional, Navigation, Robot-Assisted, and Augmented Reality Guiding Systems.International journal of spine surgery · 2024Article
- Performance evaluation of an AI-based preoperative planning software application for automatic selection of pedicle screws based on computed tomography images.Frontiers in surgery · 2023Article
- AR-Supported Supervision of Conditional Autonomous Robots: Considerations for Pedicle Screw Placement in the Future.Journal of imaging · 2022Article
- Discussion on the possibility of multi-layer intelligent technologies to achieve the best recover of musculoskeletal injuries: Smart materials, variable structures, and intelligent therapeutic planning.Frontiers in bioengineering and biotechnology · 2022Review
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 at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
introductionSeveral techniques for pedicle screw placement have been described including freehand techniques, fluoroscopy assisted, computed tomography (CT) guidance, and robotics. Image-guided surgery offers the potential to combine the benefits of CT guidance without the added radiation. This study investigated the ability of a neural network to place lumbar pedicle screws with the correct length, diameter, and angulation autonomously within radiographs without the need for human involvement. MATERIALS AND
methodsThe neural network was trained using a machine learning process. The method combines the previously reported autonomous spine segmentation solution with a landmark localization solution. The pedicle screw placement was evaluated using the Zdichavsky, Ravi, and Gertzbein grading systems.
resultsIn total, the program placed 208 pedicle screws between the L1 and S1 spinal levels. Of the 208 placed pedicle screws, 208 (100%) had a Zdichavsky Score 1A, 206 (99.0%) of all screws were Ravi Grade 1, and Gertzbein Grade A indicating no breech. The final two screws (1.0%) had a Ravi score of 2 (<2 mm breech) and a Gertzbein grade of B (<2 mm breech).
conclusionThe results of this experiment can be combined with an image-guided platform to provide an efficient and highly effective method of placing pedicle screws during spinal stabilization surgery.
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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.