Literature DB >> 31548477

Interobserver Reliability When Classifying MR Imaging of the Lumbar Spine: Written Instructions Alone Do Not Suffice.

Ulf Krister Hofmann1, Ramona Luise Keller2, Marco Gesicki1,3, Christian Walter1, Falk Mittag1.   

Abstract

PURPOSE: Numerous classification systems have been proposed to analyze lumbar spine MRI scans. When evaluating these systems, most studies draw their conclusions from measurements of experienced clinicians. The aim of this study was to evaluate the impact of specific measurement training on interobserver reliability in MRI classification of the lumbar spine.
METHODS: Various measurement and classification systems were assessed for their interobserver reliability in 30 MRIs from patients with chronic lumbar back and sciatic pain. Two observers were experienced spine surgeons. The third observer was an inexperienced medical student who, prior to the study measurements, in addition to being given the detailed written instructions also given to the surgeons, obtained a list of 20 reference measurements in MRI scans from other patients to practice with.
RESULTS: Excellent agreement was observed between the medical student and the spine surgeon who had also created the reference measurements. Between the two spine surgeons, agreement was markedly lower in all systems investigated (e.g., antero-posterior spinal canal diameter intraclass correlation coefficient [ICC] [3.1] = 0.979 vs. ICC [3.1] = 0.857).
CONCLUSION: These data warrant the creation of publicly available standardised measurement examples of accepted classification systems to increase reliability of the interpretation of MR images.

Entities:  

Keywords:  Schizas’ spinal stenosis classification; facet joint degeneration; lumbar spinal stenosis; magnetic resonance imaging; neuroforaminal stenosis

Year:  2019        PMID: 31548477     DOI: 10.2463/mrms.mp.2019-0079

Source DB:  PubMed          Journal:  Magn Reson Med Sci        ISSN: 1347-3182            Impact factor:   2.471


  2 in total

1.  Introduction and reproducibility of an updated practical grading system for lumbar foraminal stenosis based on high-resolution MR imaging.

Authors:  Elisabeth Sartoretti; Michael Wyss; Alex Alfieri; Christoph A Binkert; Cyril Erne; Sabine Sartoretti-Schefer; Thomas Sartoretti
Journal:  Sci Rep       Date:  2021-06-07       Impact factor: 4.379

2.  Spine Medical Image Segmentation Based on Deep Learning.

Authors:  Qingfeng Zhang; Yun Du; Zhiqiang Wei; Hengping Liu; Xiaoxia Yang; Dongfang Zhao
Journal:  J Healthc Eng       Date:  2021-12-15       Impact factor: 2.682

  2 in total

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