Automated detection of spinal bone marrow oedema in axial spondyloarthritis: training and validation using two large phase 3 trial datasets.
Objective To evaluate the performance of machine learning (ML) models for the automated scoring of spinal MRI bone marrow oedema (BMO) in patients with axial spondyloarthritis (axSpA) and compare them with expert scoring. Methods ML algorithms using SpineNet software were trained and validated on 34...
| Publicado en: | Rheumatology Vol. 64; no. 10; pp. 5446 - 5455 |
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| Autores principales: | , , , , , , , , , , , , , |
| Formato: | diagnostic images research tables/charts Journal Article |
| Publicado: |
Oxford University Press / USA
Oct2025
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=188554580&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 188554580 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 14620324 DN9 jtl: Rheumatology issn: 14620324 maglogo: N pubinfo: dt: Oct2025 vid: 64 iid: 10 pid: 622 pub: Oxford University Press / USA artinfo: ui: 188554580 188554580 188554580 10.1093/rheumatology/keaf323 188554580 ppf: 5446 ppct: 9 formats: tig: atl: Automated detection of spinal bone marrow oedema in axial spondyloarthritis: training and validation using two large phase 3 trial datasets. aug: au: Jamaludin, Amir Windsor, Rhydian Ather, Sarim Kadir, Timor Zisserman, Andrew Braun, Juergen Gensler, Lianne S Østergaard, Mikkel Poddubnyy, Denis Coroller, Thibaud Porter, Brian Ligozio, Gregory Readie, Aimee Machado, Pedro M affil: Visual Geometry Group, Department of Engineering Science, University of Oxford, Oxford, UK sug: subj: Axial Spondyloarthritis Radiography Bone Marrow Pathology Spine Radiography Edema Diagnosis Magnetic Resonance Imaging Methods Image Interpretation, Computer Assisted Machine Learning Algorithms Sensitivity and Specificity Predictive Value of Tests Funding Source Human Comparative Studies Validation Studies Secondary Analysis kappa Statistic Descriptive Statistics Data Analysis Software Detection Algorithms Artificial Intelligence ab: Objective To evaluate the performance of machine learning (ML) models for the automated scoring of spinal MRI bone marrow oedema (BMO) in patients with axial spondyloarthritis (axSpA) and compare them with expert scoring. Methods ML algorithms using SpineNet software were trained and validated on 3483 spinal MRIs from 686 axSpA patients across two clinical trial datasets. The scoring pipeline involved (i) detection and labelling of vertebral bodies and (ii) classification of vertebral units for the presence or absence of BMO. Two models were tested: Model 1, without manual segmentation, and Model 2, incorporating an intermediate manual segmentation step. Model outputs were compared with those of human experts using kappa statistics, balanced accuracy, sensitivity, specificity and AUC. Results Both models performed comparably to expert readers, regarding presence vs absence of BMO. Model 1 outperformed Model 2, with an AUC of 0.94 (vs 0.88), accuracy of 75.8% (vs 70.5%) and kappa of 0.50 (vs 0.31) using absolute reader consensus scoring as the external reference; this performance was similar to the expert inter-reader accuracy of 76.8% and kappa of 0.47 in a radiographic axSpA dataset. In a non-radiographic axSpA dataset, Model 1 achieved an AUC of 0.97 (vs 0.91 for Model 2), accuracy of 74.6% (vs 70%) and kappa of 0.52 (vs 0.27), comparable to the expert inter-reader accuracy of 74.2% and kappa of 0.46. Conclusion ML software shows potential for automated MRI BMO assessment in axSpA, offering benefits such as improved consistency, reduced labour costs and minimized inter- and intra-reader variability. Trial registration Clinicaltrials.gov, http://clinicaltrials.gov , MEASURE 1 study (NCT01358175); PREVENT study (NCT02696031). pubtype: Academic Journal doctype: diagnostic images research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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