AIM: To design and evaluate the impact of virtual reality (VR) pre-surgical practice on the performance of actual endodontic microsurgery. METHODOLOGY: The VR system operates on a laptop with a 1.6-GHz Intel processor and 2 GB of main memory. Volumetric cone-beam computed tomography (CBCT) data were acquired from a fresh cadaveric porcine mandible prior to endodontic microsurgery. Ten inexperienced endodontic trainees were randomized as to whether they performed endodontic microsurgery with or without virtual pre-surgical practice. The VR simulator has microinstruments to perform surgical procedures under magnification. After the initial endodontic microsurgery, all participants served as their own controls by performing another procedure with or without virtual pre-surgical practice. All procedures were videotaped and assessed by two independent observers using an endodontic competency rating scale (from 6 to 30). RESULTS: A significant difference was observed between the scores for endodontic microsurgery on molar teeth completed with virtual pre-surgical practice and those completed without virtual presurgical practice, median 24.5 (range = 17-28) versus median 18.75 (range = 14-26.5), P = 0.041. A significant difference was observed between the scores for osteotomy on a molar tooth completed with virtual pre-surgical practice and those completed without virtual pre-surgical practice, median 4.5 (range = 3.5-4.5) versus median 3 (range = 2-4), P = 0.042. CONCLUSIONS: Pre-surgical practice in a virtual environment using the 3D computerized model generated from the original CBCT image data improved endodontic microsurgery performance.
|Aim To evaluate the effectiveness of haptic virtual reality (VR)simulator training using microcomputed tomography (micro-CT) tooth models on minimizing procedural errors in endodontic access preparation.
Methodology Fourth year dental students underwent a pre-training assessment of access cavity preparation on an extracted maxillary molar tooth mounted on a phantom head. Students were then randomized to training on either the micro-CT tooth models with a haptic VR simulator (n = 16) or extracted teeth in a phantom head (n = 16) training environments for 3 days, after which the assessment was repeated. The main outcome measure was procedural errors assessed by an expert blinded to trainee and training status. The secondary outcome measures were tooth mass loss and task completion time. The Wilcoxon test was used to examine the differences between pre-training and posttraining error scores, on the same group. The Mann–Whitney test was used to detect any differences between haptic VR training and phantom head training groups. The independent t-test was used to make a comparison on tooth mass removed and task completion time between the haptic VR training and phantom head training groups.
Results Post-training performance had improved compared with pre-training performance in error scores in both groups (P < 0.05). However, error score reduction between the haptic VR simulator and the conventional training group was not significantly different (P > 0.05). The VR simulator group decreased significantly (P < 0.05) the amount of hard tissue volume lost on the post-training exercise. Task completion time was not significantly different (P > 0.05) in both groups.
Conclusions Training on the haptic VR simulator and conventional phantom head had equivalent effects on minimizing procedural errors in endodontic access cavity preparation.
We present a dental training system with a haptic interface that allows dental students or experts to practice dental procedures in a virtual environment. The simulator is able to monitor and classify the performance of an operator into novice or expert categories. The intelligent training module allows a student to simultaneously and proactively follow the correct dental procedures demonstrated by an intelligent tutor.
We present a dental training system with a haptic interface that allows dental students or experts to practice dental procedures in a virtual environment. The simulator is able to monitor and classify the performance of an operator into novice or expert categories. The intelligent training module allows a student to simultaneously and proactively follow the correct dental procedures demonstrated by an intelligent tutor. Methods: The virtual reality (VR) simulator simulates the tooth preparation procedure both graphically and haptically, using a video display and haptic device. We evaluated the performance of users using hidden Markov models (HMMs) incorporating various data collected by the simulator. We implemented an intelligent training module which is able to record and replay the procedure that was performed by an expert and allows students to follow the correct steps and apply force proactively by themselves while reproducing the procedure. Results: We find that the level of graphics and haptics fidelity is acceptable as evaluated by dentists. The accuracy of the objective performance assessment using HMMs is encouraging with 100 percent accuracy. Conclusions: The simulator can simulate realistic tooth surface exploration and cutting. The accuracy of automatic performance assessment system using HMMs is also acceptable on relatively small data sets. The intelligent training allows skill transfer in a proactive manner which is an advantage over the passive method in a traditional training. We will soon conduct experiments with more participants and implement a variety of training strategies.
Dental students devote several years to the acquisition of sufficient psychomotor skills to prepare them for entry-level dental practice. Traditional methods of dental surgical skills training and assessment are being challenged by the complications such as the lack of real-world cases, unavailability of expert supervision and the subjective manner of surgical skills assessment. To overcome these challenges, we developed a dental training system that provides a VR environment with a haptic device for dental students to practice tooth preparation procedures. The system monitors important features of the procedure, objectively assesses the quality of the performed procedure using hidden Markov models, and provides objective feedback on the user’s performance for each stage in the procedure. Important features for characterizing the quality of the procedure were identified based on interviews with experienced dentists. We evaluated the accuracy of the skill assessment with data collected from novice dental students as well as experienced dentists. We also evaluated the quality of the system’s feedback by asking a dental expert for comments. The experimental results show high accuracy in classifying users into novice and expert, and the evaluation indicated a high acceptance rate for the generated feedback.
Traditional methods of dental surgical skills training and assessment are being challenged by complications such as unavailability of expert supervision and the subjective manner of surgical skills assessment. This paper presents a dental surgical skills training system that provides a virtual reality environment with a haptic device for dental students to practice tooth preparation procedures. The system monitors important features of the procedures, objectively assesses the quality of the performed procedure and provides objective feedback on the user’s performance for each stage in the procedure. We evaluated the accuracy of the skill assessment with data collected from novice dental students as well as experienced dentists. The experimental results show high accuracy in classifying users into novice and expert. The evaluation of the system’s generated feedback also indicated a high acceptance rate.
In well-defined domains such as Physics, Mathematics, and Chemistry, solutions to a posed problem can objectively be classified as correct or incorrect. In ill-defined domains such as medicine, the classification of solutions to a patient problem as correct or incorrect is much more complex. Typical tutoring systems accept only a small set of approved solutions for each problem scenario fed to the system. Plausible student solutions that fall outside the scope of this small set of approved solutions are rejected as being incorrect, even though these solutions may be acceptable or close to acceptable. This leads to brittleness in the evaluation of student solutions. This paper describes a tutoring system for medical problem-based learning (PBL), which can accept a wide variety of plausible solutions without placing an extensive burden on knowledge acquisition. A widely available medical knowledge source is deployed as a domain ontology, and concept relationships in the ontology are used to make inferences and expand the space of plausible solutions beyond the scope of solutions explicitly provided to the system. Parent-child relationships are used to infer generalized solutions, whereas relationships of synonymy are used to infer alternate solutions. Evaluations of the system after expanding the solution space indicate accuracy close to that of human experts, who agreed among themselves with Pearson Correlation Coefficient of 0.48 and p < 0.05. The system precision dropped by 32%, while the recall increased by five times. The geometric mean of sensitivity and specificity was increased by 0.33.
Today a great many medical schools have turned to a problem-based learning (PBL) approach to teaching as an alternative to traditional didactic medical education to teach clinical-reasoning skills at the early stages of medical education. While PBL has many strengths, effective PBL tutoring is time-intensive and requires the tutor to provide a high degree of personal attention to the students, which is difficult in the current academic environment of increasing demands on faculty time. This paper describes the student modeling approach used in the COMET intelligent tutoring system for collaborative medical PBL. To generate appropriate tutorial actions, COMET uses a model of each student’s clinical reasoning for the problem domain. In addition, since problem solving in group PBL is a collaborative process, COMET uses a group model that enables it to do things like focus the group discussion, promote collaboration, and suggest peer helpers. Bayesian networks are used to model individual student knowledge and activity, as well as that of the group. The validity of the modeling approach has been tested with student models in the areas of head injury, stroke, and heart attack. Receiver operating characteristic (ROC) curve analysis shows that the models are highly accurate in predicting individual student actions. Comparison with human tutors shows that the focus of group activity determined by the model agrees with that suggested by the majority of the human tutors with a high degree of statistical agreement (McNemar test, p = 0.774, Kappa= 0.823).