An Artificial Intelligence-Based Support Tool for Lumbar Spinal Stenosis Diagnosis from Self-Reported History Questionnaire.

Frederik Abel¹, Eugene Garcia², Vera Andreeva³, Nikolai S Nikolaev⁴, Serhii Kolisnyk⁵, Ruslan Sarbaev², Ivan Novikov², Evgeniy Kozinchenko², Jack Kim², Andrej Rusakov², Raphael Mourad⁶, Darren R Lebl¹

¹Department of Spine Surgery, Hospital for Special Surgery, New York, New York, USA.

²Remedy Logic, New York, New York, USA.

³Federal State Budgetary Institution, Federal Center for Traumatology, Orthopedics and Arthroplasty, Ministry of Health of the Russian Federation, Cheboksary, Russia.

⁴Federal State Budgetary Institution, Federal Center for Traumatology, Orthopedics and Arthroplasty, Ministry of Health of the Russian Federation, Cheboksary, Russia; Federal State Budgetary Educational Institution of Higher Education, Chuvash State University named after I.N. Ulyanov, Cheboksary, Russia.

⁵Department of Physical and Rehabilitation Medicine, Vinnitsa National Medical University, Vinnytsia, Ukraine.

⁶University of Toulouse, CNRS, UPS, Toulouse, France; Remedy Logic, New York, New York, USA. Electronic address: raphael.mourad@univ-tlse3.fr.

PubMed DOI

Abstract

OBJECTIVES: Symptomatic lumbar spinal stenosis (LSS) leads to functional impairment and pain. While radiologic characterization of the morphological stenosis grade can aid in the diagnosis, it may not always correlate with patient symptoms. Artificial intelligence (AI) may diagnose symptomatic LSS in patients solely based on self-reported history questionnaires. METHODS: We evaluated multiple mac…

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