• I Gusti Lanang Ngurah Agung Artha Wiguna, Yosi Kristian, Maria Florencia Deslivia, Rudi Limantara, David Cahyadi, Ivan Alexander Liando, Hendra Aryudi Hamzah, Kevin Kusuman, Dominicus Dimitri, Maria Anastasia, and I Ketut Suyasa.
• Department of Orthopaedic Surgery, Udayana University, Prof I G N G Ngoerah Hospital Jl. Diponegoro, Dauh Puri Klod, Denpasar, Bali, 80113, Indonesia. dr_lan2002@yahoo.com.
• Eur Spine J. 2024 Nov 1; 33 (11): 420442134204-4213.

Study DesignCross-sectional Database Study.ObjectiveWhile the American Spinal Injury Association (ASIA) Impairment Scale is the standard for assessing spinal cord injuries (SCI), it has limitations due to subjectivity and impracticality. Advances in machine learning (ML) and image recognition have spurred research into their use for outcome prediction. This study aims to analyze deep learning techniques for identifying and classifying cervical SCI severity from MRI scans.MethodsThe study included patients with traumatic and nontraumatic cervical SCI admitted from 2019 to 2022. MRI images were labeled by two senior resident physicians. A deep convolutional neural network was trained using axial and sagittal cervical MRI images from the dataset. Model performance was assessed using Dice Score and IoU to measure segmentation accuracy by comparing predicted and ground truth masks. Classification accuracy was evaluated with the F1 Score, balancing false positives and negatives.ResultIn the axial spinal cord segmentation, we achieved a Dice score of 0.94 for and IoU score of 0.89. In the sagittal spinal cord segmentation, we obtained Dice score up to 0.9201 and IoU scores up to 0.8541. The model for axial image score classification gave a satisfactory result with an F1 score of 0.72 and AUC of 0.79.ConclusionOur models successfully identified cervical SCI on T2-weighted MR images with satisfactory performance. Further research is needed to develop more advanced models for predicting patient outcomes in SCI cases.© 2024. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

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