Assessment of Lumbar Lordosis Distribution with a Novel Mathematical Approach and Its Adaptation for Lumbar Intervertebral Disc Degeneration.


Journal

Computational and mathematical methods in medicine
ISSN: 1748-6718
Titre abrégé: Comput Math Methods Med
Pays: United States
ID NLM: 101277751

Informations de publication

Date de publication:
2020
Historique:
received: 03 09 2019
revised: 09 01 2020
accepted: 04 02 2020
entrez: 8 5 2020
pubmed: 8 5 2020
medline: 9 2 2021
Statut: epublish

Résumé

Low back pain and disc degeneration could be linked to global spinal geometry. Our study aimed to develop a reliable new mathematical method to assess the local distribution of total lumbar lordosis with a single numeric parameter and compare it with lumbar intervertebral disc degeneration using routine MRI scans. An online, open access, easy-to-use platform for measurements was developed based on a novel mathematical approach using MRIs of 60 patients. Our Spinalyze Software can be used online with uploaded MRIs. Several new parameters were introduced and assessed to describe variation in segmental lordosis distribution with a single numerical value. The Pfirrmann grading system was used for the classification of lumbar intervertebral disc degeneration. Relationships were investigated between the grade categories of L1-S1 lumbar discs and the MRI morphological parameters with correlation analysis. Results confirm that the determination of measurement points and calculated parameters are reliable (ICCs and Pearson Spinalyze Software based on a novel mathematical approach provides a free, easy-to-use, reliable, and online measurement tool using standard MRIs to approximate the curvature of lumbar lordosis. The new reliable

Identifiants

pubmed: 32377225
doi: 10.1155/2020/7312125
pmc: PMC7180428
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Pagination

7312125

Informations de copyright

Copyright © 2020 Zoltan Sandor et al.

Déclaration de conflit d'intérêts

The authors disclose that they have no financial or personal relationships with other people or organizations that could inappropriately influence their work. The authors declare that they have no conflicts of interest.

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Auteurs

Zoltan Sandor (Z)

Institute of Digital Health Sciences, Semmelweis University, Budapest, Hungary.

Gabor Kristof Rathonyi (GK)

Department of Orthopaedics, Health Services of Budavari Local Government, Budapest, Hungary.

Elek Dinya (E)

Institute of Digital Health Sciences, Semmelweis University, Budapest, Hungary.

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