After standard surgery for neck hernias, about 25% of patients report low satisfaction. This thesis applied inferential statistics to clinical data and Machine Learning to medical imaging, with the... Show moreAfter standard surgery for neck hernias, about 25% of patients report low satisfaction. This thesis applied inferential statistics to clinical data and Machine Learning to medical imaging, with the goal of finding out where differences in functional outcomes after surgery come from and how artificial intelligence can improve the diagnostic and prognostic process. The initial idea that differences in functional recovery were due to surgical technique was refuted by an RCT from this dissertation. The differences in functional recovery between three different surgical groups (removal of the intervertebral disc without artificial material, placement of intervertebral disc prosthesis, and fusion of vertebrae with a cage) were found to be minimal. It was striking that not surgical technique, but patients' mental health and preoperative, radiological imaging were found to be predictive of clinical recovery after surgery. Although the intervertebral disc prosthesis did not deliver on the promise of preserving mobility and thus could not prevent degeneration at adjacent levels, using Deep Learning based solely on the preoperative MRI of the neck, researchers were able to predict, among other things, which patients would require reoperation after surgery for that adjacent degeneration. The Deep Learning model did that significantly better than an experienced neuroradiologist and neurosurgeon. Such Deep Learning models eliminate the need for time-consuming questionnaires and are thus more cost-effective and less stressful for the patient, while they can be used to identify radiological features important for predicting the postoperative course. After validation with larger radiological datasets, these models can support clinical decision-making and help physicians develop personalized treatment strategies. Challenges within image analysis research for the spine lies in integrating different models into one automated process, preferably built into the electronic health record. Show less
