## 2 Literature Review
### Related Studies  
Table 1 summarizes studies on the application of ML algorithms to IONM. [PERSON] et al. (2016) presented k-nearest neighbors (KNN)- and bagged trees-based ML models to predict positive outcomes after lumbar surgeries in 55 patients. The positive outcome was defined as a motor improvement after the surgery. They compared ML-based prediction methods with pre-existing criteria (50% of transcranial motor evoked potential improvement). In their work, the ML-based method showed a relatively higher sensitivity (87.5%) but lower specificity (33.3%), which was inferior to the pre-existing criteria for predicting postoperative motor improvement. Consequently, they suggested that their proposed method had more room to advance with a large-scale study. [PERSON] et al. (2017) developed a deep learning-based automated detection system for neuromonitoring documentation. They first identified operative reports containing neuromonitoring documentation by rule-based natural language processing. Subsequently, they applied a deep learning-based natural language processing model to identify events indicating a change in status, difficulty in establishing baseline signals, and a stable course, from the reports of 993 patients who underwent spinal surgery. For detection of a change in status, they achieved an area under the receiver operating characteristic curve (AUROC) of 1.0 and an F1 score of 0.80 (discussed further below). Their results suggest that deep learning-based natural language processing models can identify medical documentation of IONM from a large volume of reports in a validated and timely manner. [PERSON] et al. (2018) predicted electromyography (EMG) signal characteristics during thyroid surgery in 34 patients. They utilized a Bayesian convolutional neural network (CNN) approach to simultaneously classify action potentials and assess signal characteristics. The extended model with two hidden layers with sigmoid activation yielded the best predictive value, with an accuracy of 97.6%. By applying a Bayesian deep learning model, they estimated the uncertainty of the model output, which improved the interpretability of the prediction. They demonstrated that the deep learning model was suited for robust interpretation of electrophysiological signals. Figure 2: Flow chart of article selection. [PERSON] et al. [19] applied a deep learning model to free-running EMG for recurrent laryngeal nerve monitoring during thyroid surgery. They classified the EMG according to morphology and presented a hybrid model that combined a CNN approach with a long short-term memory (LSTM) network. Their proposed CNN-LSTM hybrid model yielded an accuracy of 89.54% and a sensitivity of 94.23%. Their results demonstrated the possibility of reducing inter-rater variability in the reading of free-running EMGs by using deep learning models, reducing the interpretive burden on the expert. [PERSON] et al. [20] presented a model for the interpretation of outliers via train-of-four (TOF) measurements during intraoperative acceleromyosin neuromuscular monitoring. They used a cost-sensitive logistic regression model to analyze 533 TOF measurements from 35 patients. In terms of the predictive power of this model, the AUROC was 0.91 (95% confidence interval: 0.72-0.97) and the F1 score was 0.86 (0.48-0.97). Their model proved outstanding for binary classification. Their study is important since it showed that the model could analyze TOF measurements to automatically identify outliers during intraoperative accelero-myographic neuromuscular monitoring. [PERSON] et al. [21] conducted visual evoked potential (VEP) monitoring in 76 patients who underwent surgical decompression for stellar region tumors. They presented a model that could classify amplitude changes in VEPs during surgery, by combining CNN and recurrent neural network (RNN) algorithms. The target class was divided into three groups: increased VEP amplitude (>25% increase), decreased VEP amplitude (>25% decrease), and no change in VEP amplitude.