# A Review of Training and Guidance Systems in Medical Surgery
## 3 Methods
### Analysis of the Selected Works
#### 3.7.2 Comparison Analysis
Using the value of \(h=1.7\), each cluster was evaluated to find their principal characteristic. \begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline
The second cluster was formed by the works discussing surgical environments [49; 58; 59; 60; 75; 77; 84; 98; 109; 110]. The main characteristic of this cluster is the feature to store metadata, similar to cluster one. The metadata used in these simulators is mostly used for evaluation. Assistance and guidance are only covered in References [49; 58; 59; 60; 109], and the rest of the works only cover one or none, like Reference [75]. Training environments in this cluster could be considered the complete solutions that do not use artificial intelligence techniques, and only 27.77% of the applications do not provide force feedback. Finally, it has to be noted that most of the simulators in this cluster are virtual solutions, except for Reference [111]. The third cluster includes the papers in References [45; 51; 63; 65; 67; 70; 74; 78; 79; 81; 82; 87; 91; 92; 93; 96; 99; 100; 102; 104; 105; 106; 108]. In this cluster, most of the simulators mainly use haptic devices for force feedback, and these works are also used to assess students' performance. This cluster represents 43.24% of all the researches that implements force feedback in their design. The most commonly haptic device used is Geomagic Touch, due to its six degrees of freedom control. The above-presented simulators also assess user performance in the virtual environment and assistance during the task. However, only a few of them provide guidance ([74; 93; 100]). This cluster could represent early-stage simulations that have started the validation process. In this cluster, applications included should be considered as the next step to add the store metadata module to start the evaluation of the task. Additionally, to provide guidance, they should start implementing visual cues, direct messages, or markers as a visual aid, or they should implement orientation forces to improve performance during the task. The fourth cluster includes the simulators covered in References [56; 72; 97; 110]. The common group features are that they are applications that focus on assistance and store metadata. They also guide the form of visual cues or markers [56; 72; 110]. Nevertheless, force feedback implementation [97; 110] should be considered in these environments, where the interaction and user performance are usually related to the way the practitioner maneuvers in the virtual environment. An improvement to this cluster could be the implementation of a database. By only storing the session, simulators could also provide performance tracking over time. The fifth cluster characterizes the works in References [46; 47; 48; 50; 52; 53; 54; 55; 57; 62; 64; 66; 68; 69; 73; 83; 85; 86; 88; 89; 90; 94; 95; 101; 103; 107]. The main characteristic in this cluster is the use of guidance. This cluster represents 65% of the works that use guidance and 36.67% of works that are commercial solutions. Authors present in this cluster should focus future researches on providing full evaluation and assistance solutions. In-depth feedback for the users, with the addition of individual learning assessment over time, could allow students to enhance dexterity and improve performance. These modules should measure and guide them to perform the task properly, providing an enhanced learning process. Finally, it has to be noted that only the works in References [68; 69] apply AI techniques. Lastly, an interesting fact is that 45% of the researches presented here uses commercial solutions. From this percentage, 53% are physical workstations that use workstations, which are composed of physical models and virtual environments to proceed with a surgical task, 30% are CA solutions for planning or to be used during surgeries, and 17% are virtual environments that can be found in the