Introducing virtual reality technology into surgical training markedly enhances the development of surgical skills. It is critical to accurately assess surgeon proficiency post-training, as traditional evaluation methods often fail to distinguish effectively between novices and experts. Analysis of EEG signals during virtual surgical procedures, augmented by machine learning techniques, provides a robust method for improving skill assessment. The study finds that utilizing both traditional metrics and EEG rhythm indicators significantly increases classification accuracy. Specifically, when integrated with support vector machines and convolutional recurrent neural networks, these metrics enhance classification accuracy by 15.84% and 19.07%, respectively. This methodology offers a thorough tool for assessing surgical proficiency, underscoring the efficacy of EEG rhythms and machine learning in advancing surgical training evaluations.

Since the advent of virtual reality (VR), it has been implemented in medical education for surgical training and anatomy education so that the Internet of Medical Things (IoMT) could be further boosted. There have been rare studies on the research trends of the evaluation of endoscopic training through different XR modalities. Position-based dynamics (PBD) has been utilized to optimize the surgical thread simulation, This paper aims to quantitatively evaluate the training performance of each XR modality in general and in terms of the medical fields studied and outcomes measured. Sensors and devices are utilized to form the Internet of Medical Things for healthcare, where the data is uploaded to the cloud and then analyzed as follows before being fed back to the doctor so that he or she can understand his or her level of operation. Through subjective and objective evaluation, the potential promoting effects of vision and touch in module training were discussed.

The advancement of virtual reality technology has ushered in new developments in the medical field. The use of virtual surgery training simulators alleviates the paucity of training resources and high training expenses associated with traditional surgical capabilities. Regardless of the type of schooling, doctors must continue to educate themselves. The postoperative evaluation mechanism is incomplete. Traditional objective evaluation indicators are unable to meet surgeons' stringent expectations. The electroencephalograph (EEG) rhythm index is proposed in this article as a new tool for evaluating and distinguishing between novice and expert doctors. The experiment uses a cutting training module from neurosurgery training and compares it with established assessment metrics to determine the correct rate of classification of new evaluation metrics, classifying testers by both metrics and finding a 20% increase in correctness. Additionally, this article compares the energy topographic maps of different EEG rhythms of novices and experts. For classification, two-machine learning algorithms, SVM and random forest, are utilized at the same time. The findings reveal that the accuracy of distinguishing indicators based on EEG cycles is 10% higher than that of typical objective evaluation indicators, regardless of the categorization method. ROC curve analysis was also used to compare the two classification models. The AUC value for the EEG rhythm evaluation index model was 0.971, whereas the AUC value for the classic objective evaluation index model was 0.761, which explains the EEG rhythm evaluation index. The model demonstrates a categorization standard that is reliable.

Minimally invasive surgery (MIS) is gradually replacing traditional open surgery. Novices need a lot of practice in the surgical simulator to master surgical skills. Therefore, this paper developed an extended reality-based minimally invasive surgery (MIS-XR) simulator, which includes VR, AR and IVR simulators, and introduces the traditional Box simulator to compare these four simulators. Twenty-two subjects were divided into the expert group (6) and novice group (16) and were invited to participate in the experiment. Face, content, and construct validation methods were used to evaluate the tactile sense, visual sense, scene authenticity, and performance of the four simulators. The cumulative summation was used to further analyze the learning curve of 30 times training for the novice group, to verify the effectiveness of the four simulators and determine which simulator can improve the operator’s surgical skills more quickly. The results of the face and content validation show that the Box simulator is the strongest among the four simulators in the tactile sense, IVR simulator is superior to the Box simulator and VR simulator in scene authenticity. The result of construction validation shows that the four simulators are not only useful in improving the surgical skills of novices but also retain the surgical skills after a period of rest. The MIS-XR simulator developed in this paper is effective and can be used as a training device of surgical skills for novices.

Chronic kidney disease has become one of the diseases with the highest morbidity and mortality in kidney diseases, and there are still some problems in surgery. During the operation, the surgeon can only operate on two-dimensional ultrasound images and cannot determine the spatial position relationship between the lesion and the medical puncture needle in real-time. The average number of punctures per patient will reach 3 to 4, Increasing the incidence of complications after a puncture. This article starts with ultrasound-guided renal biopsy navigation training, optimizes puncture path planning, and puncture training assistance. The augmented reality technology, combined with renal puncture surgery training was studied. This paper develops a prototype ultrasound-guided renal biopsy surgery training system, which improves the accuracy and reliability of the system training. The system is compared with the VR training system. The results show that the augmented reality training platform is more suitable as a surgical training platform. Because it takes a short time and has a good training effect.