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Peer-Reviewed Publication
Eur Spine J2024;33(3):941-948.March 1, 2024Journal Article

A neural network model for detection and classification of lumbar spinal stenosis on MRI.

Vladislav Tumko1, Jack Kim2, Natalia Uspenskaia1, Shaun Honig1, Frederik Abel3, Darren R Lebl3, Irene Hotalen1, Serhii Kolisnyk4, Mikhail Kochnev1, Andrej Rusakov1, Raphaël Mourad5
1Remedy Logic, 1177 Avenue of the Americas, 5th Floor, New York, NY, 10036, USA.
2Remedy Logic, 1177 Avenue of the Americas, 5th Floor, New York, NY, 10036, USA. jack.kim@remedylogic.com.
3Hospital for Special Surgery, 535 East 70th Street, New York, NY, 10021, USA.
4Vinnitsa National Medical University, Vinnytsia, Ukraine.
5University of Toulouse, 118 Rte de Narbonne, 31062, Toulouse, France. raphael.mourad@univ-tlse3.fr.
PubMed DOI

Abstract

OBJECTIVES: To develop a three-stage convolutional neural network (CNN) approach to segment anatomical structures, classify the presence of lumbar spinal stenosis (LSS) for all 3 stenosis types: central, lateral recess and foraminal and assess its severity on spine MRI and to demonstrate its efficacy as an accurate and consistent diagnostic tool. METHODS: The three-stage model was trained on 1635…

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