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Deep phenotyping the cervical spine: automatic characterization of cervical degenerative phenotypes based on T2-weighted MRI

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Abstract

Purpose

Radiological degenerative phenotypes provide insight into a patient’s overall extent of disease and can be predictive for future pathological developments as well as surgical outcomes and complications. The objective of this study was to develop a reliable method for automatically classifying sagittal MRI image stacks of cervical spinal segments with respect to these degenerative phenotypes.

Methods

We manually evaluated sagittal image data of the cervical spine of 873 patients (5182 motion segments) with respect to 5 radiological phenotypes. We then used this data set as ground truth for training a range of multi-class multi-label deep learning-based models to classify each motion segment automatically, on which we then performed hyper-parameter optimization.

Results

The ground truth evaluations turned out to be relatively balanced for the labels disc displacement posterior, osteophyte anterior superior, osteophyte posterior superior, and osteophyte posterior inferior. Although we could not identify a single model that worked equally well across all the labels, the 3D-convolutional approach turned out to be preferable for classifying all labels.

Conclusions

Class imbalance in the training data and label noise made it difficult to achieve high predictive power for underrepresented classes. This shortcoming will be mitigated in the future versions by extending the training data set accordingly. Nevertheless, the classification performance rivals and in some cases surpasses that of human raters, while speeding up the evaluation process to only require a few seconds.

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Data, materials, and/or code availability

The material associated with the study is available upon reasonable request. The computer code and the images are confidential.

Abbreviations

T2w:

T2-Weighted

ROI:

Region of interest

AI:

Artificial intelligence

ACDF:

Anterior cervical discectomy fusion

TSE:

Turbo spin-echo

ROC:

Receiver-operator characteristic

Post.:

Posterior

Ant.:

Anterior

Sup.:

Superior

Inf.:

Inferior

Abnorm.:

Abnormality

DC:

Dilated convolutions

Res:

Residual connections

ClsW:

Class-weighted loss

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Funding

The authors did not receive support from any organization for the submitted work.

Author information

Authors and Affiliations

  1. Institute for Orthopaedic Research and Biomechanics, Trauma Research Center Ulm, University Hospital Ulm, Ulm, Germany

    Frank Niemeyer, Youping Tao & Hans-Joachim Wilke

  2. Department of Teaching, Research and Development, Schulthess Clinic, Spine Center, Lengghalde 2, 8008, Zurich, Switzerland

    Fabio Galbusera

  3. Department of Orthopaedic Surgery, Rush University Medical Center, Chicago, IL, USA

    Frank M. Phillips & Howard S. An

  4. Spine Clinic, Virginia Mason Medical Center, Seattle, WA, USA

    Philip K. Louie

  5. International Spine Research and Innovation Initiative, Rush University Medical Center, Chicago, IL, USA

    Dino Samartzis

Authors
  1. Frank Niemeyer
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  2. Fabio Galbusera
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Corresponding author

Correspondence to Fabio Galbusera.

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Conflict of interest

The authors have no relevant financial or non-financial interests to disclose.

Ethics approval

The study has been approved by the approved by Rush’s IRB as 18033101-IRB01, “Machine learning of cervical spine MRI phenotypes.”

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Since this was not a prospective study, but a retrospective study of preexisting records, patient informed consent was not required.

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Niemeyer, F., Galbusera, F., Tao, Y. et al. Deep phenotyping the cervical spine: automatic characterization of cervical degenerative phenotypes based on T2-weighted MRI. Eur Spine J 32, 3846–3856 (2023). https://doi.org/10.1007/s00586-023-07909-9

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  • DOI: https://doi.org/10.1007/s00586-023-07909-9

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