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CC BY 4.0 · Rev Bras Ortop (Sao Paulo) 2024; 59(05): e689-e695
DOI: 10.1055/s-0044-1779317
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Convolutional Neural Networks in the Diagnosis of Cervical Myelopathy

Article in several languages: português | English
Murat Korkmaz 
1   Departamento de Ortopedia e Traumatologia, Istanbul Faculty of Medicine, Istanbul University, Istambul, Turquia
,
Hakan Yılmaz 
2   Departamento de Engenharia Médica, Faculty of Engineering, Karabuk University, Karabuk, Turquia
,
Merve Damla Korkmaz 
3   Departamento de Medicina Física e Reabilitação, Istanbul Kanuni Sultan Suleyman Training and Research Hospital, University of Health Sciences, Istambul, Turquia
,
Turgut Akgül 
1   Departamento de Ortopedia e Traumatologia, Istanbul Faculty of Medicine, Istanbul University, Istambul, Turquia
› Author Affiliations Financial Support The authors declare that the present research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.
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Abstract

Objective Artificial intelligence technologies have been used increasingly in spine surgery as a diagnostic tool. The aim of the present study was to evaluate the effectiveness of the convolutional neural networks in the diagnosis of cervical myelopathy (CM) compared with conventional cervical magnetic resonance imaging (MRI).

Materials and Methods This was a cross-sectional descriptive analytical study. A total of 125 participants with clinical and radiological diagnosis of CM were included in the study. Sagittal and axial MRI images in the T2 sequence of the cervical spine were used. All image parts were obtained as 8 bytes/pixel in 2 different categories, CM and normal, both in axial and sagittal views.

Results Triple cross validation was performed to prevent overfitting during the training process. A total of 242 sample images were used for training and testing the model created for axial views. In the axial view, the calculated values are 97.44% for sensitivity and 97.56% for specificity. A total of 249 sample images were used for training and testing the model created for sagittal views. The calculated values are 97.50% for sensitivity and 97.67% for specificity. After the training, the average accuracy value was 96.7% (±1.53) for the axial view and 97.19% (±1.2) for the sagittal view.

Conclusion Deep learning (DL) has shown a great improvement especially in spine surgery. We found that DL technology works with a higher accuracy than other studies in the literature for the diagnosis of CM.

Keywords

artificial neural networks - cervical vertebrae - deep learning - magnetic resonance imaging - myelopathy

Work carried out at the Department of Physical Medicine and Rehabilitation, University of Health Sciences, Istanbul Kanuni Sultan Suleyman Training and Research Hospital, Istanbul, Turkey.




Publication History

Received: 08 March 2023

Accepted: 05 May 2023

Article published online:
07 December 2024

© 2024. The Author(s). This is an open access article published by Thieme under the terms of the Creative Commons Attribution 4.0 International License, permitting copying and reproduction so long as the original work is given appropriate credit (https://creativecommons.org/licenses/by/4.0/)

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