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External Validation of SpineNetV2 on a Comprehensive Set of Radiological Features for Grading Lumbosacral Disc Pathologies

Authors :
Nigru, Alemu Sisay
Benini, Sergio
Bonetti, Matteo
Bragaglio, Graziella
Frigerio, Michele
Maffezzoni, Federico
Leonardi, Riccardo
Source :
North American Spine Society Journal - NASSJ; 20240101, Issue: Preprints
Publication Year :
2024

Abstract

In recent years, the integration of Artificial Intelligence (AI) models has revolutionized the diagnosis of Low Back Pain (LBP) and associated disc pathologies. Among these, SpineNetV2 stands out as a state-of-the-art, open-access model for detecting and grading various intervertebral disc pathologies. However, ensuring the reliability and applicability of AI models like SpineNetV2 is paramount. Rigorous validation is essential to guarantee their robustness and generalizability across diverse patient cohorts and imaging protocols.

Details

Language :
English
ISSN :
26665484
Issue :
Preprints
Database :
Supplemental Index
Journal :
North American Spine Society Journal - NASSJ
Publication Type :
Periodical
Accession number :
ejs67812851
Full Text :
https://doi.org/10.1016/j.xnsj.2024.100564