## 3 Results
### Results of Individual Studies  
#### 3.3.1 Outcome: Preoperative Planning  
Three studies were included (one prospective comparative, one prospective case-control, one retrospective cohort) [27; 32; 33]. The authors of these studies reported that VR and AR were both excellent tools for preoperative planning. These two technologies allowed the surgeon to act in a fast, precise manner, providing an additional degree of safety when approaching the surgical phase [2; 3]. Specifically, AR was useful in the visualization of preoperative planning images directly overlaying them on the patient. The most frequently utilized hardware were head-mounted displays (HMDs), including Magic Leap One, backed by Google and Microsoft HoloLens. Two articles were found regarding AI use in preoperative planning. The former depicted its use in lumbar spinal stenosis detection [33]. The purpose of this study was to classify central lumbar spinal stenosis in four grades--none, mild, moderate and severe. This classification was compared with the independent readings of a spinal surgeon. The \begin{table}
\begin{tabular}{c c c c c c c c c c c c c} \hline \hline [PERSON], 2018 & 2 & 2 & 0 & 2 & 0 & 0 & 0 & 2 & 0 & 0 & 0 & 0 & 8 \\ \hline [PERSON], 2018 & 2 & 2 & 2 & 2 & 0 & 2 & 2 & 2 & 0 & 2 & 2 & 2 & 20 \\ \hline [PERSON], 2020 & 2 & 2 & 0 & 2 & 0 & 0 & 0 & 2 & 2 & 2 & 0 & 0 & 12 \\ \hline [PERSON], 2018 & 2 & 0 & 0 & 2 & 0 & 0 & 0 & 0 & 0 & 0 & 2 & 6 \\ \hline [PERSON], 2019 & 2 & NA & 0 & 2 & 2 & 2 & 0 & NA & 2 & 2 & 0 & 0 & 12 \\ \hline [PERSON], 2020 & 2 & 0 & 2 & 1 & 1 & 2 & 2 & 0 & 2 & 2 & 0 & 16 \\ \hline [PERSON], 2019 & 2 & 2 & 0 & 2 & 0 & 2 & 2 & 2 & 0 & 0 & 0 & 0 & 10 \\ \hline [PERSON], 2020 & 2 & 2 & 0 & 2 & 0 & 2 & 2 & 2 & 2 & 2 & 2 & 20 \\ \hline [PERSON], 2020 & 2 & 2 & 2 & 0 & 0 & 0 & 2 & 2 & 2 & 0 & 0 & 12 \\ \hline [PERSON], 2017 & 2 & NA & 0 & 2 & 0 & 0 & 0 & NA & 0 & 0 & 0 & 2 & 6 \\ \hline [PERSON], 2018 & 2 & 2 & 0 & 2 & 2 & 2 & 2 & 2 & 2 & 2 & 0 & 20 \\ \hline [PERSON], 2019 & 2 & 2 & 0 & 2 & 2 & 2 & 2 & 2 & 2 & 2 & 2 & 22 \\ \hline [PERSON], 2014 & 2 & 0 & 2 & 2 & 2 & 2 & 2 & 2 & 0 & 2 & 0 & 2 & 18 \\ \hline [PERSON], 2018 & 2 & 2 & 2 & 2 & 2 & 0 & 0 & 2 & 2 & 2 & 2 & 20 \\ \hline [PERSON], 2021 & 2 & NA & 0 & 2 & 2 & 0 & 0 & NA & 0 & 0 & 0 & 0 & 6 \\ \hline [PERSON], 2020 & 2 & 2 & 0 & 2 & 0 & 0 & 0 & 2 & 2 & 2 & 2 & 16 \\ \hline [PERSON], 2019 & 2 & 2 & 0 & 2 & 0 & 0 & 0 & 2 & 2 & 2 & 2 & 2 & 16 \\ \hline [PERSON], 2018 & 2 & 2 & 0 & 2 & 0 & 2 & 2 & 2 & 2 & 2 & 2 & 2 & 18 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: MINORS tool for assessing the risk of bias in non-randomized trials. system can learn how to grade the severity of lumbar stenosis with outstanding results, providing many possible uses in epidemiological studies. The second article described the accuracy of AI in predicting, diagnosing, and classifying patients affected by cervical spondylotic myelopathy (CSM) [27]. The purpose of this study was mainly diagnostic, as it aimed to understand how neural networks can be used to predict CSM severity. Two different neural network models were employed to complete these tasks. Both models showed high accuracy percentages. According to MINORS, the overall quality of evidence in these studies was assessed in the range between \"low\" and \"high\".