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Autonomous Establishment of CT‐Independent Sections to Obtain Optimum Pedicle‐Screw Axis in Direction, Length, and Safety Margin

T. A. Dwarakanath, Gaurav Bhutani, Dwarakanath Srinivas

Year
2025
Citations
1
Access
Open access

Abstract

BACKGROUND: Accurate perception of pedicle geometry during pedicle-screw placement surgery is critically important because the margin-for-error is small. METHOD: An assessment algorithm is developed to provide machine-independent MultiPlanar Reconstruction (MiMPR) of the pedicle. The reconstruction is independent of the CT-machine frame and enhances patient data portability. Additionally, the algorithm obtains the pedicle-screw axis with optimum direction, length, and margin using MPRs. A method for the autonomous identification of four body features to form a CT-independent vertebral frame, {V}, in the image space is formulated. RESULT: Applied to 200 high-resolution CT images, the approach achieved a 100% success rate in defining the pedicle-medial axis and maximum screw diameter considering the safety margin of 2 mm. CONCLUSIONS: The method eliminates subjective assessment. It provides objective assessment in determining the pedicle-medial axis with optimal direction and margin without human annotation. Additionally, it significantly enhances screw placement accuracy in robot-assisted spinal fusion surgeries, regardless of vertebra orientation.

Keywords

Margin (machine learning)Computer scienceVertebraOrientation (vector space)Artificial intelligenceOrthodonticsMathematicsMedicineSurgeryGeometry

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