Prediction of Invasive Cervical Spine Surgery Success by a Convolutional Neural Network Algorithm

A Novel Application of Machine Learning in Computer Aided Decision-Making in the Field of Neurosurgery

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The aim of this research is to build a machine learning model in order to predict the success of invasive surgical treatment on a degenerated cervical spine, based on the baseline X-ray images. The purpose of the results of this research is an application in computer-aided diagnostics and treatment planning in the field of neurosurgery. Spinal degeneration can be described as the gradual loss of spinal structure and a decreased functioning of the spine over time. Before the diagnosis of spine degeneration can be made, a sagittal X-ray analysis of the spine is very important. With the current ageing population and the relatively high prevalence of neck pain and spinal complaints of approximately 30%, there is a great demand on MRI and X-ray analysis in healthcare. Machine learning techniques, and especially convolutional neural networks, seem promising for the application of X-ray image analysis. This research was preceded by a literature study about cervical degeneration and the implementation of ML in spine research. Of the available machine learning techniques, artificial neural networks show the best classification accuracy when it comes to image classification. Convolutional Neural Networks (CNNs) in particular are applicable for the computer vision classification task of this study. That is why, in consultation with the neurosurgery department of the Leiden University Medical Center (LUMC), it was decided to focus on the development of a Convolutional Neural Network (CNN) to perform the binary classification task. The final configuration of the convolutional neural network (CNN) consists of four convolutional layers with ReLU activation function and a maximal pooling function. Batch normalization was applied after the first convolutional layer of the model in order to create a more stable training environment. The false positive rate (FPR) is 19% on average and 15% during the best performing run. The ROC curve shows an AUC during the best performing run of 0.91 and 0.86 on average, whereby 1.0 would be a perfect classifier. It can be concluded that this classification task could be performed by a convolutional neural network (CNN), adapted to the specific classification task. The lowest achieved false positive rate of the model was 15%, which shows a major improvement compared to the current clinical situation at the department of neurosurgery in the Leiden University Medical Center, where about 25% of invasive spinal surgical operations, involving cervical degeneration, are of no benefit to the patient.