Machine Learning in Intraoperative Neurophysiological Monitoring: Comparison
Please note this is a comparison between Version 2 by Sirius Huang and Version 1 by Dougho Park.

Intraoperative neurophysiological monitoring (IONM) is being applied to a wide range of surgical fields as a diagnostic tool to protect patients from neural injuries that may occur during surgery. However, several contributing factors complicate the interpretation of IONM, and it is labor- and training-intensive. Meanwhile, machine learning (ML)-based medical research has been growing rapidly, and many studies on the clinical application of ML algorithms have been published.

  • intraoperative neurophysiological monitoring
  • artificial intelligence
  • machine learning
  • deep learning

1. Introduction

Intraoperative neurophysiological monitoring (IONM) is an essential diagnostic tool for the improvement of patient safety via detection of neurological changes during surgery. IONM is currently being applied in various types of surgery, such as open cranial surgery, spinal decompression, head and neck surgery, and peripheral nerve surgery [1,2,3][1][2][3]. The most prominent advantage of IONM is its use to confirm functional integrity in real time during surgery. When a warning sign occurs, an immediate rescue intervention can be performed in the operating room, minimizing neural injuries and enabling rapid postoperative treatment [4].
However, despite its advantages, some limitations exist in interpreting IONM. In particular, several factors complicate the real-time interpretation of multimodal signals during IONM. In interpreting neurophysiological changes related to surgical factors, a multidisciplinary approach between surgeons and physiatrists is essential, and substantial information sharing between them is vital for accurate interpretation [5]. Further, non-surgical factors, such as anesthesia, the general condition of the patient, and mechanical defects, have to be considered simultaneously with surgical factors [6]. Another hurdle in interpreting IONM is that experts may interpret the same results differently [7]. Therefore, the performance and interpretation of IONM require a substantial level of training to minimize inter-rater variability. Lastly, to respond sensitively to neural deterioration that occurs during surgery, the expert must maintain high concentration even during long operations. Consequently, IONM is a complicated, labor-intensive, and expensive process (Figure 1) [8].
Figure 1. Schematic illustration of intraoperative neurophysiological monitoring (IONM). A multidisciplinary approach between the surgeon, physiatrist, and anesthesiologist is necessary throughout the process. Several confounding factors, such as surgical, anesthesiologic, and mechanical factors, as well as the patient’s condition and inter-rater variability complicate the interpretation of IONM.
The use of machine learning (ML) in medical research and clinical practice is rapidly expanding [9]. In particular, ML is increasingly being used for diagnosis and prognosis [10[10][11],11], as well as for disease classification, replacing existing methods [10]. Moreover, ML can execute proxy decision-making at the level of medical experts [12] and can readily and efficiently handle a large number of samples and variables [13]. Artificial intelligence (AI) models have the additional benefit of continuously improving themselves by learning from additional data and by applying more advanced techniques [14]. Although their performance depends on data quality and learning algorithms, in general, ML models have yielded promising results in clinical medicine [10,15][10][15].

2. Related Studies on the Application of ML Algorithms to IONM

Table 1 summarizes studies on the application of ML algorithms to IONM. Jamaludin et al. [16] 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. Agaronnik et al. [17] 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. Kortus et al. [18] 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. Zha 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. Verdonck et al. [20] presented a model for the interpretation of outliers via train-of-four (TOF) measurements during intraoperative acceleromyographic 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. Qiao et al. [21] conducted visual evoked potential (VEP) monitoring in 76 patients who underwent surgical decompression for sellar 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. In this study, the overall accuracy of multiclass classification was 87.4% (84.2–90.1%). The sensitivities for classification of no change in VEP, increasing VEP, and decreasing VEP were 92.6%, 78.9%, and 83.7%, respectively, and their specificities were 80.5%, 93.3%, and 100.0%, respectively. Somatosensory evoked potential (SEP) is a modality that acts as the framework of intraoperative spinal surgery monitoring [4]. Fan et al. [22] utilized least squares and multi-support vector regression models on 15 patients undergoing spinal surgery to intraoperatively interpret the SEP results. They defined the warning criteria as an amplitude reduction of ≥50% or a latency delay of ≥10%. Target outcomes were classified as successful, false-positive, or trauma cases. Their intelligent decision system lowered the false warning rate compared with their conventional method and enabled more accurate detection of spinal cord trauma. The multi-support vector regression model performed better than the least squared support vector regression model. Table 1 summarizes studies on the application of ML algorithms to IONM.
Table 1.
 The application of machine learning in the field of intraoperative neurophysiological monitoring.

References

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