# A Review of Training and Guidance Systems in Medical Surgery
## 3 Methods
### Synthesis of the Selected Works
#### 3.8.4 Laparoscopy
This data is used to determine the expertise level of students by applying machine learning algorithms. In this case, HMMs and FL are used. Other simulators focus only on guiding and aid [72; 73]. [PERSON] et al. developed a resectoscope simulator, which aims to let users understand the manipulation of tools and organs during myoma resection. The system directs the user during the simulation. Users have to interact with four targets, one at a time. The target point stays in red until the user successfully touches it. The simulator stores operation time, outcomes from objectives, and errors; however, this data is not shown to the student. Adding additional assessment parameters to the system, the authors could provide a proper evaluation to give learning feedback after sessions, which could enhance the user's understanding and knowledge in this type of operation. [PERSON] et al. designed video laparoscopic training exercises and developed the eLaparo4D system. eLaparo4D was constructed as a node.js application server, and it uses HTML5 with the Unity3D engine plugin to give users an online platform. The authors used the Blender framework for graphic rendering. For haptic interactions, three Geomagic Touch haptic devices are used. Two are used as tool handlers (grasper, hook, or scissors), and the third one is used to move the camera within the virtual abdomen. This approach tries to simulate realistic laparoscopic equipment's interactions. [PERSON] et al. used an Arduino board connected to a vibrating motor to enhance haptic feedback in the form of vibration. Additionally, eLaparo4D has a module for remote guidance. This guidance can be provided via the web to show students the proper way to execute a critical task; however, another eLaparo4D simulator has to be used. Scores are calculated in a way where each exercise has allowed and not allowed actions, which adds or subtracts points. Finally, user profiles are implemented in the simulator to check user progress over time. One of the most appointed systems on commercial stations is the da Vinci station manufactured by Intuitive Surgical. It costs around 1.5 to 2 million USD. [PERSON] et al. evaluated if the system can be applied in a training program [74]. [PERSON] et al. used the da Vinci Skill simulator system. The da Vinci skill simulator provides task performance assessment by using quizzes and evaluation metrics during the simulation. Moreover, the simulator guides users by providing instructions on-screen, visual prompts, and markers for aiming during the simulation. [PERSON] et al. considered that the following tasks had to be assessed for this course: camera control, clutching, handle of EndoWrist, needle driving, energy coagulation, and dissection. Performance results showed that participants obtained 80 or higher scores in the tasks, and users' post-test from users highlighted the acceptance of simulators in training courses. Another commercial station in laparoscopy that can be found in the market is SimSurgery. Nissen developed it. [PERSON] et al. evaluated this system to test its usability for training courses [75]. SimSurgery consists of a screen and two laparoscopic tools that provide force feedback to give as much realism as possible in the simulator. During the virtual procedure, on-screen guidance is provided to the user. SimSurgery counts with collision detection techniques that are used to evaluate if collisions between instruments occur. If this type of collision happens during a session, the simulator lights up a red warning on the screen. This type of visual help was implemented to teach users how to operate correctly during real operations. At the end of the simulation, time, instrument collisions, and tip trajectory are used to assess the procedure and provide a score. Studies such as the ones made by [PERSON] et al. and [PERSON] et al. reinforce that users who perform surgeries, via conventional, CA, or robot-assisted approaches, should perform runs on virtual environments to secure their level of prior experience in the task.