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Research ArticleSpecial Issue

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

Kai-Uwe LewandrowskI, Narendran Muraleedharan, Steven Allen Eddy, Vikram Sobti, Brian D. Reece, Jorge Felipe ramírez León and Sandeep Shah
International Journal of Spine Surgery December 2020, 14 (s3) S86-S97; DOI: https://doi.org/10.14444/7131
Kai-Uwe LewandrowskI
1Staff Orthopaedic Spine Surgeon Center for Advanced Spine Care of Southern Arizona and Surgical Institute of Tucson, Tucson, Arizona
MD
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Narendran Muraleedharan
2Aptus Engineering, Inc, Scottsdale, Arizona, and Multus Medical, LLC, Phoenix, Arizona
BASME
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Steven Allen Eddy
3Multus Medical, LLC, Phoenix, Arizona
MD
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Vikram Sobti
4Innovative Radiology, PC, River Forest, Illinois
MD, MBA
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Brian D. Reece
5The Spine and Orthopedic Academic Research Institute, Lewisville, Texas
MD
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Jorge Felipe ramírez León
6Fundación Universitaria Sanitas, Bogotá, Colombia, Research Team, Centro de Columna. Bogotá, Colombia, Centro de Cirugía de Mínima Invasión, CECIMIN—Clínica Reina Sofía, Bogotá, Colombia
MD
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Sandeep Shah
7Multus Medical, LLC, Phoenix, Arizona
MSEE, MBA
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Article Information

vol. 14 no. s3 S86-S97
DOI 
https://doi.org/10.14444/7131
PubMed 
33298549

Published By 
International Journal of Spine Surgery
Online ISSN 
2211-4599
History 
  • Published online December 9, 2020.

Copyright & Usage 
This manuscript is generously published free of charge by ISASS, the International Society for the Advancement of Spine Surgery. Copyright © 2020 ISASS

Author Information

  1. Kai-Uwe LewandrowskI, MD1,
  2. Narendran Muraleedharan, BASME2,
  3. Steven Allen Eddy, MD3,
  4. Vikram Sobti, MD, MBA4,
  5. Brian D. Reece, MD5,
  6. Jorge Felipe ramírez León, MD6 and
  7. Sandeep Shah, MSEE, MBA7
  1. 1Staff Orthopaedic Spine Surgeon Center for Advanced Spine Care of Southern Arizona and Surgical Institute of Tucson, Tucson, Arizona
  2. 2Aptus Engineering, Inc, Scottsdale, Arizona, and Multus Medical, LLC, Phoenix, Arizona
  3. 3Multus Medical, LLC, Phoenix, Arizona
  4. 4Innovative Radiology, PC, River Forest, Illinois
  5. 5The Spine and Orthopedic Academic Research Institute, Lewisville, Texas
  6. 6Fundación Universitaria Sanitas, Bogotá, Colombia, Research Team, Centro de Columna. Bogotá, Colombia, Centro de Cirugía de Mínima Invasión, CECIMIN—Clínica Reina Sofía, Bogotá, Colombia
  7. 7Multus Medical, LLC, Phoenix, Arizona
  1. Corresponding Author:
    Kai-Uwe Lewandrowski, MD, Staff Orthopaedic Spine Surgeon Center for Advanced Spine Care of Southern Arizona and Surgical Institute of Tucson, AZ 85712. Phone: (520) 204-1495; Fax: (623) 218-1215; Email: busniess{at}tucsonspine.com.
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Feasibility of Deep Learning Algorithms for Reporting in Routine Spine Magnetic Resonance Imaging
Kai-Uwe LewandrowskI, Narendran Muraleedharan, Steven Allen Eddy, Vikram Sobti, Brian D. Reece, Jorge Felipe ramírez León, Sandeep Shah
International Journal of Spine Surgery Dec 2020, 14 (s3) S86-S97; DOI: 10.14444/7131

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Feasibility of Deep Learning Algorithms for Reporting in Routine Spine Magnetic Resonance Imaging
Kai-Uwe LewandrowskI, Narendran Muraleedharan, Steven Allen Eddy, Vikram Sobti, Brian D. Reece, Jorge Felipe ramírez León, Sandeep Shah
International Journal of Spine Surgery Dec 2020, 14 (s3) S86-S97; DOI: 10.14444/7131
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More in this TOC Section

  • Letter to the Editor: Rasch Analysis and High Value Spinal Endoscopy—Another Perspective
  • Potential Applications of Artificial Intelligence and Machine Learning in Spine Surgery Across the Continuum of Care
  • Real-World Implementation of Artificial Intelligence/Machine Learning for Managing Surgical Spine Patients at 2 Academic Health Care Systems
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Keywords

  • artificial intelligence
  • deep neural network learning
  • magnetic resonance imaging
  • spinal pathologies
  • feasibility analysis

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