Feasibility of Deep Learning Algorithms for Reporting in Routine Spine Magnetic Resonance Imaging

@article{Lewandrowski2020FeasibilityOD,
  title={Feasibility of Deep Learning Algorithms for Reporting in Routine Spine Magnetic Resonance Imaging},
  author={K. Lewandrowski and N. Muraleedharan and Steven Allen Eddy and V. Sobti and Brian D. Reece and Jorge Felipe Ram{\'i}rez Le{\'o}n and Sandeep Shah},
  journal={International Journal of Spine Surgery},
  year={2020},
  volume={14},
  pages={S86 - S97}
}
ABSTRACT Background: Artificial intelligence is gaining traction in automated medical imaging analysis. Development of more accurate magnetic resonance imaging (MRI) predictors of successful clinical outcomes is necessary to better define indications for surgery, improve clinical outcomes with targeted minimally invasive and endoscopic procedures, and realize cost savings by avoiding more invasive spine care. Objective: To demonstrate the ability for deep learning neural network models to… Expand

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