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Abstract #2691

Classification of annular fissures and pain-positive discograms using multiple MRI-features with Machine Learning

Kerstin Lagerstrand1,2, Hanna Hebelka1,3, Leif Thorén1,3, Christian Waldenberg1,2, and Helena Brisby1,4
1Institute of Clinical Sciences, Gothenburg University, Gothenburg, Sweden, 2Medical Physics and Biomedical Engineering, Sahlgrenska University Hospital, Gothenburg, Sweden, 3Radiology, Sahlgrenska University Hospital, Gothenburg, Sweden, 4Orthopaedics, Sahlgrenska University Hospital, Gothenburg, Sweden

Imaging-based features are needed to improve the characterization of degenerative IVD-changes and possibility of finding a linkage between features and pain.

Multiple T2w-imaging-features and Machine-Learning was used for classification of fissures involving outer annulus and for pain-positive discograms.

Fissures were classified with high accuracy/precision using regional/heterogeneity features with/without axial loading of the spine. For pain-positive discograms, a larger number of such MRI-features contributed to the classification.

Findings suggest that multiple MRI-features, extracted from T2w-imaging, improve the classifications, and that regional/heterogeneity features extracted with both conventional imaging with the spine unloaded and with axial loading of the spine are of importance.

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