Open AccessAutonomous lumbar spine pedicle screw planning using machine learning: A validation study
Author(s) -
Kris Siemionow,
Craig Forsthoefel,
Michael Foy,
Dominik Gaweł,
Christian J Luciano
Publication year - 2021
Publication title -
journal of craniovertebral junction and spine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.38
H-Index - 23
eISSN - 0976-9285
pISSN - 0974-8237
DOI - 10.4103/jcvjs.jcvjs_94_21
Subject(s) - medicine , fluoroscopy , lumbar , landmark , lumbar spine , radiography , robotics , artificial intelligence , surgery , robot , computer science
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.