Siriwan Suebnukarn

A collaborative intelligent tutoring system for medical problem-based learning

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 COMETs 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).

A Bayesian approach to generating tutorial hints in a collaborative medical problem-based learning system

OBJECTIVES Today a great many medical schools have turned to a problem-based learning (PBL) approach to teaching. While PBL has many strengths, effective PBL 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 intelligent tutoring in a collaborative medical tutor for PBL. The main contribution of our work is the development of representational techniques and algorithms for generating tutoring hints in PBL group problem solving, as well as the implementation of these techniques in a collaborative intelligent tutoring system, COMET. The system combines concepts from computer-supported collaborative learning with those from intelligent tutoring systems. METHODS AND MATERIALS The system uses Bayesian networks to model individual student clinical reasoning, as well as that of the group. The prototype system incorporates substantial domain knowledge in the areas of head injury, stroke and heart attack. 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. In order to evaluate the appropriateness and quality of the hints generated by our system, we compared the tutoring hints generated by COMET with those of experienced human tutors. We also compared the focus of group activity chosen by COMET with that chosen by human tutors. RESULTS On average, 74.17% of the human tutors used the same hint as COMET. The most similar human tutor agreed with COMET 83% of the time and the least similar tutor agreed 62% of the time. Our results show that COMETs 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). The focus of group activity chosen by COMET agrees with that chosen by the majority of the human tutors with a high degree of statistical agreement (McNemar test, p=0.774, kappa=0.823). CONCLUSION Bayesian network clinical reasoning models can be combined with generic tutoring strategies to successfully emulate human tutor hints in group medical PBL.

A Systematic Evaluation of the Quality of Meta-analyses in Endodontics

INTRODUCTION Meta-analyses have been suggested to be the highest form of evidence available to clinicians to guide clinical practice in dental care. High methodologic quality is a prerequisite for valid interpretation and application of review findings. However, meta-analyses are complex exercises, and assessing quality can be a daunting task. Clinicians and policymakers require guidance, which is not provided adequately by the available literature on the quality of meta-analyses. The purpose of this study was to systematically evaluate the quality of meta-analyses that address topics pertinent to endodontics. METHODS To identify potentially eligible meta-analyses for inclusion, systematic searches performed in MEDLINE and the Cochrane Database of Systematic Reviews were enriched by hand searches, citation mining, and expert recommendation. Comprehensive search strategies were constructed for electronic searches. Predetermined inclusion criteria were applied to each identified meta-analysis independently by two reviewers. To assess report quality, the included meta-analyses were assessed by using A Measurement Tool to Assess Systematic Reviews (AMSTAR). RESULTS A total of 16 reports of meta-analyses were included (kappa = 0.96). The overall quality of reports of meta-analyses was found to be high, with an estimated mean overall AMSTAR score of 8.33 out of 11 (95% confidence interval, 7.62-8.88). The weakest areas within the included meta-analyses were failure to report the likelihood of publication bias. CONCLUSIONS The overall quality of the reports of meta-analyses available in endodontics is high according to AMSTAR.

Intelligent dental training simulator with objective skill assessment and feedback

OBJECTIVE We present a dental training simulator that provides a virtual reality (VR) environment with haptic feedback for dental students to practice dental surgical skills in the context of a crown preparation procedure. The simulator addresses challenges in traditional training such as the subjective nature of surgical skill assessment and the limited availability of expert supervision. METHODS AND MATERIALS We identified important features for characterizing the quality of a procedure based on interviews with experienced dentists. The features are patterns combining tool position, tool orientation, and applied force. The simulator monitors these features during the procedure, objectively assesses the quality of the performed procedure using hidden Markov models (HMMs), and provides objective feedback on the users performance in each stage of the procedure. We recruited five dental students and five experienced dentists to evaluate the accuracy of our skill assessment method and the quality of the systems generated feedback. RESULTS The experimental results show that HMMs with selected features can correctly classify all test sequences into novice and expert categories. The evaluation also indicates a high acceptance rate from experts for the systems generated feedback. CONCLUSION In this work, we introduce our VR dental training simulator and describe a mechanism for providing objective skill assessment and feedback. The HMM is demonstrated as an effective tool for classifying a particular operator as novice-level or expert-level. The simulator can generate tutoring feedback with quality comparable to the feedback provided by human tutors.

COMET: A Collaborative Tutoring System for Medical Problem-Based Learning

This paper discussed about the developed collaborative intelligent tutoring system for medical PBL called Comet (collaborative medical tutor). Comet uses Bayesian networks to model the knowledge and activity of individual students as well as small groups. It applies generic tutoring algorithms to these models and generates tutorial hints that guide problem solving. An early laboratory study shows a high degree of agreement between the hints generated by Comet and those of experienced human tutors. Evaluations of Comets clinical-reasoning model and the group reasoning path provide encouraging support for the general framework.

Augmented reality haptics system for dental surgical skills 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.

Haptic Virtual Reality for Skill Acquisition in Endodontics

INTRODUCTION Haptic virtual reality (VR) has revolutionized the skill acquisition in dentistry. The strength of the haptic VR system is that it can automatically record the outcome and associated kinematic data on how each step of the task is performed, which are not available in the conventional skill training environments. The aim of this study was to assess skill acquisition in endodontics and to identify process and outcome variables for the quantification of proficiency. METHODS Twenty novices engaged in the experimental study that involved practicing the access opening task with the haptic VR system. Process (speed, force utilization, and bimanual coordination) and outcome variables were determined for assessing skill performance. These values were compared before and after training. RESULTS Significant improvements were observed through training in all variables. A unique force used pattern and bimanual coordination were observed in each step of the access opening in the posttraining session. The novices also performed the tasks considerably faster with greater outcome within the first two to three training sessions. CONCLUSIONS The study objectively showed that the novices could learn to perform access opening tasks faster and with more consistency, better bimanual dexterity, and better force utilization. The variables examined showed great promise as objective indicators of proficiency and skill acquisition in haptic VR.

Access cavity preparation training using haptic virtual reality and microcomputed tomography tooth models.

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 post-training 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.

A Virtual Reality Simulator for Teaching and Evaluating Dental Procedures

OBJECTIVES 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.

Influence of graft quality and marginal bone loss on implants placed in maxillary grafted sinus: a finite element study.

The purpose of this study was to investigate the biomechanical effects of graft stiffness and progression of marginal bone loss (MBL) in the bone surrounding an implant placed in a maxillary grafted sinus based on the finite element method. The simulating model of graft stiffness as well as depth of MBL was varied to simulate nine different clinical scenarios. The results showed that the high-level strain distributions in peri-implant tissue increased with the increase in MBL depth when the stiffness of the graft was less than that of the cancellous bone (less stiffness graft models). The strain energy density (SED) value showed that a slight MBL depth (1.3 mm) with medium stiffness of grafted bone can reach the optimal load sharing due to the exhibited similar values of SED in the crestal cortical, cancellous, and grafted bone. With progression of MBL and the decrease in graft quality, maximal displacement of the implant increased considerably. Our results demonstrated that the effects of the two investigated factors (progression of MBL and graft stiffness) on the biomechanical adaptation are likely to be interrelated. The results also reveal that for clinical situations with poor grafted bone quality and progression of MBL, it is critical to consider implant stability.