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Complex surgical procedures, highly costs and time to prepare for the conventional surgical training are the factors which contribute to the development of virtual surgery training system in medical studies. A new jig and fixture has been designed to help orthopedic surgeons perform the knee replacement surgery. The jig and fixture is used to align the cutter blade in the right position before the sawing begins. It can also reduce the overall time taken when using computer assisted surgery system. Thus, training are required to practice the right techniques to apply the newly jig so that the time required to place the jig at the optimum position can be reduced. As an alternative to the conventional method, a virtual training system was developed to enhance the learning experience of novice surgeon. A testing with 8 novice surgeons showed that the time to align the jig on tibia after implementing the virtual training system were decreased compared to before. This showed that the virtual training system managed to train the doctors on the correct technique to use the newly designed jig and fixture.

Surgical training systems based on virtual reality (VR) are highly desired as they offer a cost effective and efficient alternative compared to traditional training methods. Traditional surgical training methods require cadavers or plastic models which are costly. Cadavers cannot be used repeatedly and training with plastic models cannot provide the realistic experience. This paper describes a visualization to show the use of newly design jig and fixture for computer-assisted knee replacement surgery. Orthopedic surgeons found it difficult to align the existing jig with the computer-assisted device during the operation and it is time consuming to place it at the right position. A newly design jig and fixture has been proposed to solve this problem. Visualization is needed to show the surgeons on how it will be used in the computer-assisted knee replacement surgery. Virtual models used in this visualization are constructed from the actual equipment and real human dataset.