Background context: Intraoperative neuromonitoring (IONM) has proven effective in reducing postoperative neurological complications. However, current understanding of IONM is limited and its precise meaning in relation to neurological outcomes remains unclear. Machine learning (ML) is a promising solution to analyze the excessive amount of IONM data quickly, objectively and in real-time.
Purpose: The goal is to develop a ML algorithm that can effectively predict neurological outcomes after spinal surgery using IONM data that include both motor evoked potentials (MEPs) and somatosensory evoked potentials (SSEPs), and analyze its key predicting features. To more effectively determine the specific independent contribution of both separate modalities, a separate ML model will be created for both MEP and SSEP in addition to a combined MEP-SSEP model.
Study setting: Retrospective study.
Patient sample: A total of 67 patients were analyzed.
Outcome measures: The neurological status three months postoperatively compared to the preoperative status, categorized into three classes: 'Neurological stable deficits', ‘Neurologically intact’ and 'Neurological improvement'.
Methods: 260 features were obtained from patients who underwent spinal surgery monitored by IONM. During nested cross-validation, the data was split into five folds, for both the inner and the outer loop. The four ML classifiers developed were support vector machine, K-nearest neighbors, random forest and extreme gradient boosting, and tested along the three modalities MEP, SSEP, and MEP-SSEP combination.
Results: Extreme gradient boosting outperformed the other classifiers on all performance metrics. The combined MEP-SSEP model exhibited the highest scores for sensitivity: 70.4%, specificity: 88.3% and accuracy: 87.1%, while the MEP model exhibited the highest performance for precision: 75.6%. Highest predicting scores per individual class were also obtained by this XGBoost classifier on the combined MEP-SSEP model. Key predicting features were the presence or absence of preoperative neurological deficits and last measured signal latency compared to baseline, with a contribution of 29% and 13.5% in the best performing model, respectively.
Conclusion: A reliable prediction of neurological outcomes three months postoperatively can be made combining MEP and SSEP IONM features, provided that the patient's preoperative status is accurately documented and included in the prediction. Though either MEP or SSEP features alone offer predictive value, MEP features show superior predictive values compared to SSEP features when both modalities are accessible, with latency emerging as a prominent predictive IONM feature.

Introduction: Deep brain stimulation (DBS) is an important therapeutic option for various neurological diseases. For certain indications the optimal target cannot be identified on structural magnetic resonance imaging (MRI), but can be visualized with tractography. A recent improvement for clinical practice is the probabilistic tractography algorithm, providing superior results to the deterministic counterpart. However, it is suggested that clinical users are unfamiliar with the complex technology of this new approach, which might lead to missed potential of tractography in DBS-care. The overall aims of this thesis were threefold: to create an overview of the national landscape on the application of tractography, to narrow the gap between medicine and advanced technology in the application of tractography in DBS surgery planning, and to report potentialities and pitfalls of the implementation of tractography in a general hospital.

Methods: In the first part, a survey was conducted among Dutch DBS clinicians on the deployment of tractography. The survey consisted of 25 questions about the provision of DBS care, the use of tractography, the expert’s opinion and the respondent’s demographic characteristics. A comprehensive literature study is conducted in the second part. In the third part, an economic evaluation is performed by analyzing the costs and benefits of tractography. Guidelines are suggested based on literature and expert experience.

Results: Tractography is considered valuable for essential tremor (p<0.001) and not valuable for epilepsy (p=0.002) and chronic cluster headache (p=0.016). The majority uses deterministic approaches like DTI. Probabilistic users consider tractography more valuable than deterministic users (p=0.036). There is a heterogeneity in used image acquisition parameters and a lack of knowledge exist on technical background of tractography. The key elements of the technical principles of tractography are summarized, including diffusion weighted MRI, the concept of estimating the diffusion tensors, image acquisition parameters and tract reconstruction with the deterministic and probabilistic approach and quantitative measures.

Conclusion: Tractography is used by DBS clinicians with limited knowledge on the technical aspects. Generally the inferior deterministic approach is used without an (inter)nationally standardized protocol for image acquisition and tract reconstruction. A comprehensive explanation of the technical concepts and suggested practical guidelines should contribute to better application of this promising technology in DBS-care.