ArticleInternational journal of computer assisted radiology and surgery2018
A deep learning framework for segmentation and pose estimation of pedicle screw implants based on C-arm fluoroscopy.
Article in International journal of computer assisted radiology and surgery, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers, 1 of them a synthesis that pooled it.
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Who cites it
27 citing papers in PubMed, 1 synthesis or guideline pooled it, 60 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
- Patch-Wise Approach with Vision Transformer for Detecting Implant Failure in Spinal Radiography.Journal of imaging informatics in medicine · 2026Article
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- Artificial Intelligence Applications in Musculoskeletal Imaging.Current reviews in musculoskeletal medicine · 2025Review
- AI and machine learning in paediatric spine deformity surgery.Bone & joint open · 2025Article
- A fully automatic fiducial detection and correspondence establishing method for online C-arm calibration.International journal of computer assisted radiology and surgery · 2025Article
- Evaluation of Pedicle Screw Position on Computerized Tomography Using Three-Dimensional Reconstruction Software.Medicina (Kaunas, Lithuania) · 2024Article
- Development and Validation of Deep Learning Preoperative Planning Software for Automatic Lumbosacral Screw Selection Using Computed Tomography.Bioengineering (Basel, Switzerland) · 2024Article
- Development and validation of deep learning models for identifying the brand of pedicle screws on plain spine radiographs.JOR spine · 2024Article
- Deep Learning-Based Localization and Orientation Estimation of Pedicle Screws in Spinal Fusion Surgery.Korean journal of neurotrauma · 2024Article
- Evaluation of single-stage vision models for pose estimation of surgical instruments.International journal of computer assisted radiology and surgery · 2023Article
- Artificial Intelligence in Scoliosis: Current Applications and Future Directions.Journal of clinical medicine · 2023Review
- Automatic 3D Postoperative Evaluation of Complex Orthopaedic Interventions.Journal of imaging · 2023Article
- An X-Ray C-Arm Guided Automatic Targeting System for Histotripsy.IEEE transactions on bio-medical engineering · 2023Article
- Bone density optimized pedicle screw insertion.Frontiers in bioengineering and biotechnology · 2023Article
- Current Applications of Machine Learning in Spine: From Clinical View.Global spine journal · 2022Review
- Artificial intelligence in spine care: current applications and future utility.European spine journal : official publication of the European Spine Society, the European Spinal Deformity Society, and the European Section of the Cervical Spine Research Society · 2022Review
- A Hybrid 3D-2D Image Registration Framework for Pedicle Screw Trajectory Registration between Intraoperative X-ray Image and Preoperative CT Image.Journal of imaging · 2022Article
- Current development and prospects of deep learning in spine image analysis: a literature review.Quantitative imaging in medicine and surgery · 2022Review
Corrections and comments
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Authors and funding
5 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
purposePedicle screw fixation is a challenging procedure with a concerning rates of reoperation. After insertion of the screws is completed, the most common intraoperative verification approach is to acquire anterior-posterior and lateral radiographic images, based on which the surgeons try to visually assess the correctness of insertion. Given the limited accuracy of the existing verification techniques, we identified the need for an accurate and automated pedicle screw assessment system that can verify the screw insertion intraoperatively. For doing so, this paper offers a framework for automatic segmentation and pose estimation of pedicle screws based on deep learning principles.
methodsSegmentation of pedicle screw X-ray projections was performed by a convolutional neural network. The network could isolate the input X-rays into three classes: screw head, screw shaft and background. Once all the screw shafts were segmented, knowledge about the spatial configuration of the acquired biplanar X-rays was used to identify the correspondence between the projections. Pose estimation was then performed to estimate the 6 degree-of-freedom pose of each screw. The performance of the proposed pose estimation method was tested on a porcine specimen.
resultsThe developed machine learning framework was capable of segmenting the screw shafts with 93% and 83% accuracy when tested on synthetic X-rays and on clinically realistic X-rays, respectively. The pose estimation accuracy of this method was shown to be [Formula: see text] and [Formula: see text] on clinically realistic X-rays.
conclusionsThe proposed system offers an accurate and fully automatic pedicle screw segmentation and pose assessment framework. Such a system can help to provide an intraoperative pedicle screw insertion assessment protocol with minimal interference with the existing surgical routines.
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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.