Indirect vision through a mirror, while bi-manually manipulating both the mirror and another tool is a relatively common way to perform operations in various types of surgery. However, learning such psychomotor skills requires extensive training; they are difficult to teach; and they can be quite costly, for instance, for dentistry schools. In order to study the effectiveness of VR simulators for learning these kinds of skills, we developed a simulator for training dental surgery procedures, which supports tracking of eye gaze and tool trajectories (mirror and drill), as well as automated outcome scoring. We carried out a pre-/post-test study in which 30 fifth-year dental students received six training sessions in the access opening stage of the root canal procedure using the simulator. In addition, six experts performed three trials using the simulator. The outcomes of drilling performed on realistic plastic teeth showed a significant learning effect due to the training sessions. Also, students with larger improvements in the simulator tended to improve more in the real-world tests. Analysis of the tracking data revealed novel relationships between several metrics w.r.t. eye gaze and mirror use, and performance and learning effectiveness: high rates of correct mirror placement during active drilling and high continuity of fixation on the tooth are associated with increased skills and increased learning effectiveness. Larger time allocation for tooth inspections using the mirror, i.e., indirect vision, and frequency of inspection are associated with increased learning effectiveness. Our findings suggest that eye tracking can provide valuable insights into student learning gains of bi-manual psychomotor skills, particularly in indirect vision environments.

Background This work presents a novel Ergonomic Trainer System that provides personalized pre-operative and intra-operative ergonomic feedback to improve dental work posture.Methods The system consists of two ultrasonic sensors to measure the height of operator’s seat and dental unit; two accelerometers to measure the angles of the operator’s neck and upper arm; and software to analyze data from each operator and provide a personalized pre-operative and intra-operative ergonomic feedback. Sixteen dental students were randomly assigned into a crossover trial, scheduled for each of two sequences of working: receiving feedback or no-feedback from the system while performing tooth preparation. The parameters measured were the extensions of the neck and upper arm, before (pretest) and after (posttest) receiving feedback or no-feedback from the system.Results The posttest angles of neck and upper arm had decreased significantly compared with pretest angles in the group that received preoperative and intra-operative ergonomic feedback from the system (p < .05).Conclusion Performing the dental treatment with pre-operative and intra-operative feedback had effects on improving the posture of the dental students by decreasing the angles of neck and upper arm.

With the minute margins of error in endodontic surgery, training in manual dexterity and proper instrument handling are crucial components in the dental curriculum. Important parameters include tool path, tool angulation, and force applied. In this work, we focus on training of correct application of force. This is particularly challenging since the amounts of force used are on the order of tenths of Newtons, requiring a highly refined tactile sense and incorrect force can cause irreversible damage. Too great a force can cause overdrilling or in extreme cases perforation of the tooth. Too small a force can cause thermal irritation possibly resulting in tissue necrosis. Despite the importance of correct use of force, this is the dimension on which students receive the least tutorial feedback since force information is typically not available in traditional training settings. In this paper, we present an approach to using haptic feedback as a means to convey formative feedback on the correct application of force. Feedback is conveyed to the student graphically and the correct amount of force to apply is trained haptically. The simulator is rewound and the student is asked to redo the stage where the error occurred. Preliminary evaluation against a control group of students who received only feedback concerning outcome shows the feedback mechanism to be effective.

Use of simulation to teach decision making in surgery is challenging partly due to the situated nature of the decisions, with situation awareness playing a critical role in making high quality decisions. Thus simulation systems need to be able to provide the key cues needed in making decisions with high fidelity. In this paper we present the first version of Desitra, a simulation environment for teaching decision making in dental surgery. System design was driven by an observational study of teaching sessions for endodontic surgery in the operating room which identified perceptual cues used in decision making as well as tutorial intervention strategies used by surgeons. Desitra provides an open environment for learning decision making -- students carry out dental procedures and are free to make mistakes. The pedagogical module monitors the student actions and intervenes when students make mistakes, providing as little guidance as necessary to keep students on a productive learning path. The system is implemented to run on Android tablets to be maximally accessible. Preliminary evaluation of the system shows that Desitra effectively captures key perceptual cues.

Numerous VR simulators have been developed as a means of addressing limitations of the traditional apprenticeship approach to dental surgical skill training. Most existing simulators support intra- and extra-coronal procedures such as carries removal. In this paper we address the problem of automated outcome assessment for endodontic surgery. Outcome assessment is an essential component of any system that provides formative feedback, which requires assessing the outcome, relating it to the procedure, and communicating in a language natural to dental students. This paper takes a first step toward automated generation of such comprehensive feedback. Our system automatically computes reference templates based on tooth anatomy, which provides flexibility to adjust parameters such as tolerance and to create new templates on demand. Detailed scores are transformed into the standard scoring language used by dental schools. Preliminary evaluation of our system on fifteen outcome samples with three expert endodontists shows a high degree of agreement with expert scores.

Problem based learning is becoming widely popular as an effective teaching method in medical education. Paying individual attention to a small group of students in medical PBL can place burden on the workload of medical faculty whose time is very costly. Intelligent tutoring systems offer a cost effective alternative in helping to train the students, but they are typically prone to brittleness and the knowledge acquisition bottleneck. Existing tutoring systems accept a small set of approved solutions for each problem scenario stored into the system. Plausible student solutions that lie outside the scope of the explicitly encoded ones receive little acknowledgment from the system. Tutoring hints are also confined to the knowledge space of the approved solutions, leading to brittleness in the tutoring approach. We report a tutoring system for medical PBL that employs the widely available medical knowledge source UMLS as the domain ontology. We exploit the structure of the ontology to expand the plausible solution space and generate hints based on the problem solving context. Evaluation of student learning outcomes led to highly significant learning gains (Mann-Whitney, p<0.001).

We developed an augmented reality (AR) dental training simulator utilizing a haptic (force feedback) device. A number of dental procedures such as crown preparation and opening access to the pulp can be simulated with various shapes of dental drill. The system allows students to practise surgery in the correct postures as in the actual environment by combining 3D tooth and tool models upon the real-world view and displaying the result through a video see-through head mounted display (HMD). The system monitors the important features such as applied forces and tool movement that characterize the quality of the procedure. Automatic performance assessment is achieved by comparing outcome and process features of a student with the best matching expert. Moreover, we incorporated kinematic feedback and hand guidance by haptic device. The result from an initial evaluation shows that the simulator is promising for supplemental training.

We have developed a virtual reality (VR) and an augmented reality (AR) dental training simulator utilizing a haptic device. The simulators utilize volumetric force feedback computation and real time modification of the volumetric data. They include a virtual mirror to facilitate indirect vision during a simulated operation. The AR environment allows students to practice surgery in correct postures by combining the 3D tooth and tool models with the real-world view and displaying the result through a video see-through head-mounted display (HMD). Preliminary results from an initial evaluation show that the system is a promising tool to supplement dental training and that there are advantages of the AR over the VR approach.

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 this paper, we present a novel collaborative authoring tool that was designed to allow medical teachers to formalize and visualize their knowledge for medical intelligent tutoring systems. Our goal is to increase the efficiency and effectiveness in creating the domain model - often referred to as the bottleneck in developing intelligent tutoring systems. We incorporate the Unified Medical Language System (UMLS) knowledge base to assist the authors in creating the problem solution collaboratively via the videoconferencing platform. The system consists of a share workspace gathering information visualization and tools necessary for collaborative problem-solving tasks. We found that the authoring tool can be used as a standalone program to effectively elicit the knowledge structure of the domain model. This was achieved in hours compared to months for the conventional paper-based approach.

This paper describes COMET, a collaborative intelligent tutoring system for medical problem-based learning. The system uses Bayesian networks to model individual student knowledge and activity, as well as that of the group. It incorporates a multi-modal interface that integrates text and graphics so as to provide a rich communication channel between the students and the system, as well as among students in the group. Students can sketch directly on medical images, search for medical concepts, and sketch hypotheses on a shared workspace. The prototype system incorporates substantial domain knowledge in the area of head injury diagnosis. A major challenge in building COMET has been to develop algorithms for generating tutoring hints. Tutoring in PBL is particularly challenging since the tutor should provide as little guidance as possible while at the same time not allowing the students to get lost. From studies of PBL sessions at a local medical school, we have identified and implemented eight commonly used hinting strategies. We compared the tutoring hints generated by COMET with those of experienced human tutors. Our results show that COMET's hints agree with the hints of the majority of the human tutors with a high degree of statistical agreement (McNemar test, p = 0.652, Kappa = 0.773).