Autonomous lumbar spine pedicle screw planning using machine learning: A validation study.

Introduction: Several 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.

Augmented reality and artificial intelligence-assisted surgical navigation: Technique and cadaveric feasibility study.

Purpose: Augmented reality-based image overlay of virtual bony spine anatomy can be projected onto real spinal anatomy using computer tomography-generated DICOM images acquired intraoperatively. The aim of the study was to develop a technique and assess the accuracy and feasibility of lumbar vertebrae pedicle instrumentation using augmented reality-assisted surgical navigation.

Subjects And Methods: An augmented reality and artificial intelligence (ARAI)-assisted surgical navigation system was developed.