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Peer-Reviewed Publication
Diagn Interv Imaging2023;104(7-8):343-350.January 1, 2023Observational Study

Automatic deep learning-based assessment of spinopelvic coronal and sagittal alignment.

Mohamed Zerouali1, Alexandre Parpaleix2, Mansour Benbakoura2, Caroline Rigault2, Pierre Champsaur3, Daphné Guenoun4
1Department of Radiology, Institute for Locomotion, Sainte-Marguerite Hospital, APHM, 13009 Marseille, France.
2Milvue, 75014 Paris, France.
3Department of Radiology, Institute for Locomotion, Sainte-Marguerite Hospital, APHM, 13009 Marseille, France; Institute of Movement Sciences (ISM), CNRS, Aix Marseille University, 13005 Marseille, France.
4Department of Radiology, Institute for Locomotion, Sainte-Marguerite Hospital, APHM, 13009 Marseille, France; Institute of Movement Sciences (ISM), CNRS, Aix Marseille University, 13005 Marseille, France. Electronic address: daphne.guenoun@ap-hm.fr.
PubMed DOI

Abstract

PURPOSE: The purpose of this study was to evaluate an artificial intelligence (AI) solution for estimating coronal and sagittal spinopelvic alignment on conventional uniplanar two-dimensional whole-spine radiograph. MATERIAL AND METHODS: This retrospective observational study included 100 patients (35 men, 65 women) with a median age of 14 years (IQR: 13, 15.25; age range: 3-64 years) who underwe…

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