The increasing use of virtual reality (VR) simulators in surgical training makes it imperative that definitive studies be performed to assess their training effectiveness. Indeed, in this paper we report the meta-analysis of the efficacy of virtual reality simulators in: 1) the transference of skills from the simulator training environment to the operating room, and 2) their ability to discriminate between the experience levels of their users. The task completion time and the error score were the two study outcomes collated and analyzed in this meta-analysis. Sixteen studies were identified from a computer-based literature search (1996-2004). The meta-analysis of the random effects model (because of the heterogeneity of the data) revealed that training on virtual reality simulators did lessen the time taken to complete a given surgical task as well as clearly differentiate between the experienced and the novice trainees. Meta-analytic studies such as the one reported here would be very helpful in the planning and setting up of surgical training programs and for the establishment of reference 'learning curves' for a specific simulator and surgical task. If any such programs already exist, they can then indicate the improvements to be made in the simulator used, such as providing for more variety in their case scenarios based on the state and/or rate of learning of the trainee.

Abstract. This contribution extends learning vector quantization to the domain of graphs. For this, we first identify graphs with points in some orbifold, then derive a generalized differentiable intrinsic metric, and finally extend the update rule of LVQ for generalized differentiable distance metrics. First experiments indicate that the proposed approach can perform comparable to state-of-the-art methods in structural pattern recognition.

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This contribution proposes a new approach towards developing a class of probabilistic methods for classifying attributed graphs. The key concept is random attributed graph, which is defined as an attributed graph whose nodes and edges are annotated by random variables. Every node/edge has two random processes associated with it-occurence probability and the probability distribution over the attribute values. These are estimated within the maximum likelihood framework. The likelihood of a random attributed graph to generate an outcome graph is used as a feature for classification. The proposed approach is fast and robust to noise.