Effectiveness of the Multilayered Caries Model and Visuo-tactile Virtual Reality Simulator for Minimally Invasive Caries Removal: A Randomized Controlled Trial.

This work presents the multilayered caries model with a visuo-tactile virtual reality simulator and a randomized controlled trial protocol to determine the effectiveness of the simulator in training for minimally invasive caries removal. A three-dimensional, multilayered caries model was reconstructed from 10 micro-computed tomography (CT) images of deeply carious extracted human teeth before and after caries removal. The full grey scale 0-255 yielded a median grey scale value of 0-9, 10-18, 19-25, 26-52, and 53-80 regarding dental pulp, infected carious dentin, affected carious dentin, normal dentin, and normal enamel, respectively. The simulator was connected to two haptic devices for a handpiece and mouth mirror. The visuo-tactile feedback during the operation varied depending on the grey scale. Sixth-year dental students underwent a pretraining assessment of caries removal on extracted teeth. The students were then randomly assigned to train on either the simulator (n=16) or conventional extracted teeth (n=16) for 3 days, after which the assessment was repeated. The posttraining performance of caries removal improved compared with pretraining in both groups (Wilcoxon, p<0.05). The equivalence test for proportional differences (two 1-sided t-tests) with a 0.2 margin confirmed that the participants in both groups had identical posttraining performance scores (95% CI=0.92, 1; p=0.00). In conclusion, training on the micro-CT multilayered caries model with the visuo-tactile virtual reality simulator and conventional extracted tooth had equivalent effects on improving performance of minimally invasive caries removal.

The use of cone-beam computed tomography and virtual reality simulation for pre-surgical practice in endodontic microsurgery.

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.

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.

Supporting multi-level multi-perspective dynamic decision making in medicine.

Most medical decision problems are exceedingly complex and contain a large number of variables. Abstraction facilitates the process of building a decision model by allowing a model builder to work at a level of detail that he is most comfortable with; it is also useful in time-critical situations or when there is insufficient data to support complete specification of probabilities of the uncertain events. In this paper, we identify and formalize abstraction and refinement operations commonly used in model construction. We illustrate the use of these mechanisms with an example on the follow-up management of colorectal cancer patients after surgery.

Efficient temporal probabilistic reasoning via context-sensitive model construction.

We present a language for representing context-sensitive temporal probabilistic knowledge. Context constraints allow inference to be focused on only the relevant portions of the probabilistic knowledge. We provide a declarative semantics for our language. We present a sound and complete algorithm for computing posterior probabilities of temporal queries, as well as an efficient implementation of the algorithm. Throughout we illustrate the approach with the problem of reasoning about the effects of medications and interventions on the state of a patient in cardiac arrest. We empirically evaluate the efficiency of our system by comparing its inference times on problems in this domain with those of standard Bayesian network representations of the problems.

BANTER: a Bayesian network tutoring shell.

We present an educational tool for bringing the information contained in a Bayesian network to the end user in an easily intelligible form. The BANTER shell is designed to tutor users in evaluation of hypotheses and selection of optimal diagnostic procedures. BANTER can be used with any Bayesian network containing nodes that can be classified into hypotheses, observations, and diagnostic procedures. The system enables one to present various types of queries to the network, to test one's ability to select optimal diagnostic procedures, and the request explanations. We describe the system's capabilities by illustrating how it functions with two structurally different network models of real-world medical problems.

Construction of a Bayesian network for mammographic diagnosis of breast cancer.

Bayesian networks use the techniques of probability theory to reason under uncertainty, and have become an important formalism for medical decision support systems. We describe the development and validation of a Bayesian network (MammoNet) to assist in mammographic diagnosis of breast cancer. MammoNet integrates five patient-history features, two physical findings, and 15 mammographic features extracted by experienced radiologists to determine the probability of malignancy. We outline the methods and issues in the system's design, implementation, and evaluation. Bayesian networks provide a potentially useful tool for mammographic decision support.

Decision-theoretic refinement planning in medical decision making: management of acute deep venous thrombosis.

Decision-theoretic refinement planning is a new technique for finding optimal courses of action. The authors sought to determine whether this technique could identify optimal strategies for medical diagnosis and therapy. An existing model of acute deep venous thrombosis of the lower extremities was encoded for analysis by the decision-theoretic refinement planning system (DRIPS). The encoding represented 6,206 possible plans. The DRIPS planner used artificial intelligence techniques to eliminate 5,150 plans (83%) from consideration without examining them explicitly. The DRIPS system identified the five strategies that minimized cost and mortality. The authors conclude that decision-theoretic planning is useful for examining large medical-decision problems.

A knowledge-based model construction approach to medical decision making.

We present a framework for representing the probabilistic effects of actions and contingent treatment plans. Our language has a well-defined declarative semantics and we have developed an implemented algorithm (named BNG) that generates Bayesian networks (BN) to compute the posterior probabilities of queries. In this paper we address the problem of projecting a contingent treatment plan by automatically constructing a structure of interrelated BNs, which we call a BN-graph, and applying the available propagation procedures on it. To address the optimal plan generation, we base our approach on the observation that normally the target plan space has a well-defined structure. We provide a language to describe plan spaces which resembles a programming language with loops and conditionals. We briefly present the procedures for finding the optimal plan(s) from such specified plan spaces.

Optimizing, diagnostic, and therapeutic strategies using decision-theoretic planning: principles and applications.

OBJECTIVE: Decision-theoretic planning is a new technique for selecting optimal actions. The authors sought to determine whether decision-theoretic planning could be applied to medical decision making to identify optimal strategies for diagnosis and therapy. METHODS: An existing model of acute deep venous thrombosis (DVT) of the lower extremities--in which 24 management strategies were compared--was converted into a set of conditional-probabilistic actions for use by the DRIPS decision-theoretic planning system. Actions were grouped into an abstraction/decomposition hierarchy. A utility function was defined in accordance with the existing DVT management model to incorporate the costs and risks of the diagnostic tests and treatments. RESULTS: From 18 primitive actions (such as "perform venography" and "treat if venography shows thigh DVT"), a total of 312 possible concrete plans were encoded within the abstraction/decomposition hierarchy. The DRIPS planning system used abstraction techniques to eliminate 136 possible plans (44%) from consideration. It determined that, given the parameters specified, the most cost-effective management strategy was "no tests, no treatment." This result differed from the published result of "perform ultrasonography, treat if positive." In reviewing the original article, it was determined that DRIPS had revealed an error in the manually constructed decision trees used in that manuscript. At values of $75,000 and greater for the cost of death, the optimal strategy became "impedance plethysmography (IPG), don't wait, perform venography if IPG is positive, and treat only if venography shows thigh DVT." CONCLUSION: Decision-theoretic planning is applicable to medical decision making and may be an extremely useful technique for complex decisions. The use of inheritance abstraction makes the technique computationally tractable for complex planning problems, and the modular nature of the data entry may help eliminate errors that appear in manually encoded decision trees.

Clinical simulation using context-sensitive temporal probability models.

We present a language for representing context-sensitive temporal probabilistic knowledge. Context constraints allow inference to be focused on only the relevant portions of the probabilistic knowledge. We provide a declarative semantics for our language and an implemented algorithm (BNG) that generates Bayesian networks to compute the posterior probabilities of queries. We illustrate the use of the BNG system by applying it to the problem of modeling the effects of medications and other interventions on the condition of a patient in cardiac arrest.

Preliminary investigation of a Bayesian network for mammographic diagnosis of breast cancer.

Bayesian networks use the techniques of probability theory to reason under conditions of uncertainty. We investigated the use of Bayesian networks for radiological decision support. A Bayesian network for the interpretation of mammograms (MammoNet) was developed based on five patient-history features, two physical findings, and 15 mammographic features extracted by experienced radiologists. Conditional-probability data, such as sensitivity and specificity, were derived from peer-reviewed journal articles and from expert opinion. In testing with a set of 77 cases from a mammography atlas and a clinical teaching file, MammoNet performed well in distinguishing between benign and malignant lesions, and yielded a value of 0.881 (+/- 0.045) for the area under the receiver operating characteristic curve. We conclude that Bayesian networks provide a potentially useful tool for mammographic decision support.

Decision-theoretic refinement planning: a new method for clinical decision analysis.

Clinical decision analysis seeks to identify the optimal management strategy by modelling the uncertainty and risks entailed in the diagnosis, natural history, and treatment of a particular problem or disorder. Decision trees are the most frequently used model in clinical decision analysis, but can be tedious to construct, cumbersome to use, and computationally prohibitive, especially with large, complex decision problems. We present a new method for clinical decision analysis that combines the techniques of decision theory and artificial intelligence. Our model uses a modular representation of knowledge that simplifies model building and enables more fully automated decision making. Moreover, the model exploits problem structures to yield better computational efficiency. As an example we apply our techniques to the problem of management of acute deep venous thrombosis.

A Bayesian network model for radiological diagnosis and procedure selection: work-up of suspected gallbladder disease.

Bayesian networks, a technique for reasoning under uncertainty, currently are being developed for application to medical decision making. To explore their usefulness for radiologic decision support, a Bayesian belief network was constructed in the domain of hepatobiliary disease. The network model's nodes represent diagnoses, physical findings, laboratory test results, and imaging study findings. The connections between nodes incorporate conditional probabilities, such as sensitivity and specificity, to represent probabilistic influences. Statistical data were abstracted from peer-reviewed journal articles on hepatobiliary disease, and a network was created to reflect the data. The network successfully determined the a priori probabilities of various diseases, and incorporated laboratory and imaging results to calculate the a posteriori probabilities. The most informative examination was identified, that is, the laboratory study or imaging procedure that led to the greatest diagnostic certainty. Bayesian networks represent a very promising technique for decision support in radiology: they can assist physicians in formulating diagnoses and in selecting imaging procedures.

Generating explanations and tutorial problems from Bayesian networks.

We present a system that generates explanations and tutorial problems from the probabilistic information contained in Bayesian belief networks. BANTER is a tool for high-level interaction with any Bayesian network whose nodes can be classified as hypotheses, observations, and diagnostic procedures. Users need no knowledge of Bayesian networks, only familiarity with the particular domain and an elementary understanding of probability. Users can query the knowledge base, identify optimal diagnostic procedures, and request explanations. We describe BANTER's algorithms and illustrate its application to an existing medical model.