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"Deep Learning on Preoperative Radiographs for Clinical Success Prediction after Surgery for Cervical Degenerative Disease"
C.M.W Goedmakers, L.M. Pereboom, M.L. de Leeuw den Bouter, R.F. Remis, M. Staring and C.L.A. Vleggeert-Lankamp
Abstract
As populations age and the prevalence of cervical spine degeneration rises, the demand for computer-aided diagnostics and prognostics in neurosurgery rises. Not all patients benefit from surgical treatment and predicting who will remains challenging. Automating parts of the radiological image analysis process using Machine Learning could provide more accurate, consistent assessment with increased time efficiency, and potentially gain new disease insights. The purpose of this study was to identify which image features on cervical radiographs are important for the prediction of clinical success one year after surgery for cervical disc disease, by developing and validating a deep learning algorithm that predicts clinical success solely based on the radiograph.
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Copyright © 2023 by the authors.
Published version © 2023 by .
Personal use of this material is permitted. However, permission to reprint or republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works, must be obtained from the copyright holder.
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BibTeX entry
| @article{Goedmakers:2023, |
| author |
= {Goedmakers, C.M.W and Pereboom, L.M. and de Leeuw den Bouter, M.L. and Remis, R.F. and Staring, M. and Vleggeert-Lankamp, C.L.A.}, |
| title |
= {Deep Learning on Preoperative Radiographs for Clinical Success Prediction after Surgery for Cervical Degenerative Disease}, |
| journal |
= {Brain and Spine}, |
| volume |
= {3}, |
| pages |
= {101842}, |
| year |
= {2023}, |
| } |
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| last modified: 18-12-2023 |
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