Efficient mining of emerging patterns: discovering trends and differences
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Virtual temporal bone dissection: a case study
Proceedings of the conference on Visualization '01
CAEP: Classification by Aggregating Emerging Patterns
DS '99 Proceedings of the Second International Conference on Discovery Science
Real-time haptic and visual simulation of bone dissection
Presence: Teleoperators and Virtual Environments - special issue: IEEE virtual reality 2002 conference
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
Mining statistically important equivalence classes and delta-discriminative emerging patterns
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
Towards Haptic Performance Analysis Using K-Metrics
HAID '08 Proceedings of the 3rd international workshop on Haptic and Audio Interaction Design
Enhancing Realism of Wet Surfaces in Temporal Bone Surgical Simulation
IEEE Transactions on Visualization and Computer Graphics
A hierarchical approach to real-time activity recognition in body sensor networks
Pervasive and Mobile Computing
Classification of surgical processes using dynamic time warping
Journal of Biomedical Informatics
Contrast Data Mining: Concepts, Algorithms, and Applications
Contrast Data Mining: Concepts, Algorithms, and Applications
Surgical gesture classification from video data
MICCAI'12 Proceedings of the 15th international conference on Medical Image Computing and Computer-Assisted Intervention - Volume Part I
| Hi-index | 0.00 |
Delivering automated real-time performance feedback in simulated surgical environments is an important and challenging task. We propose a framework based on patterns to evaluate surgical performance and provide feedback during simulated ear (temporal bone) surgery in a 3D virtual environment. Temporal bone surgery is composed of a number of stages with distinct aims and surgical techniques. To provide context-appropriate feedback we must be able to identify each stage, recognise when feedback is to be provided, and determine the nature of that feedback. To achieve these aims, we train pattern-based models using data recorded by a temporal bone simulator. We create one model to predict the current stage of the procedure and separate stage-specific models to provide human-friendly feedback within each stage. We use 27 temporal bone simulation runs conducted by 7 expert ear surgeons and 6 trainees to train and evaluate our models. The results of our evaluation show that the proposed system identifies the stage of the procedure correctly and provides constructive feedback to assist surgical trainees in improving their technique.