Computing and Information Systems - Theses
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ItemReal-time feedback for surgical simulation using data mining( 2014)Surgical trainees devote years to master the surgical skills required to safely perform surgery. Traditionally, they refine their psychomotor skills by practising on plastic bones or cadavers under the supervision of expert surgeons. Experts guide trainees through surgical procedures while providing feedback on the quality of their procedure. However, there are limitations to this approach, which include a shortage of cadaver bones, limited availability of expert supervision, and the subjective manner of surgical skill assessment. To address these limitations, the introduction of new techniques such as 3D illusion, haptic feedback and augmented reality have significantly improved the realism of surgical simulators. Such simulators have the potential to provide a cost-effective platform, which allows trainees to practice many surgical cases of varying difficulty, and provides the flexibility of practising repeatedly at their own convenience. However, most simulators lack the automated performance assessment and feedback, which limits the applicability of simulators as self-guided training systems. In thesis, we aim to deliver automated performance assessment and feedback in a virtual simulation environment. The automated performance assessment provides information on the quality of surgical result, which is a critical component for a self-guided training platform. A large number of recent studies have focused on scoring the outcome of surgical tasks. However, this score typically based on the result of a surgical task (such as the shape of a surgical end-product) and ignores the rich information provided by real-time performance attributes, such as motion records. Furthermore, since this assessment is delivered at the end of each task, it does not allow any opportunity to identify and address mistakes as they occur. We propose an event-based framework that provides online assessment with different temporal granularities. The evaluations show that the proposed framework provides accurate performance assessment using both motion records and end-product information. Although automated performance assessment provides expertise score to illustrate the surgical perform, a single score has limited utility in improving surgical technique, which can be equally important. Trainees need constructive human understandable feedback to refine their psychomotor skills. To this end, we propose a Random Forest based approach to generate meaningful automated real-time performance feedback. Our evaluation demonstrates it can significantly improve the surgical techniques. However, this random forest based method makes specific assumptions that all drilling movements made by experts are of "expert quality'' and all operations made by trainees are suboptimal. This hampers the model training process and leads to lower accuracy rates. Therefore we propose a pattern-based approach to capture the differences in technique between experts and trainees and deliver real-time feedback to improve performance, while avoiding the assumption of "polarising'' the quality of drill strokes based on expertise. Our evaluation results show that the proposed approach identifies the stage of the surgical procedure correctly and provides constructive feedback to assist surgical trainees in improving their technique. Another challenge for automated performance assessment is hard to extend existing evaluation models to new specimens. In order to train reliable assessment models for new specimens, the classical machine learning approaches require a new set of human expert examples collected from each new specimen. To eliminate this need, we propose a transfer learning framework to adapt a classifier built on a single specimen to multiple specimens. Once a classifier is trained, we translate the new specimens' features to the original feature space, which allows us to carry out performance evaluation on different specimens using the same classifier. In summary, the major contributions of this thesis involve the development of self-guided training platform for delivering automatic assessment and feedback using data mining techniques.