Purpose Analyzing surgical activities has received a growing interest in recent years. Several methods have been proposed to identify surgical activities and surgical phases from data acquired in operating rooms. These context-aware systems have multiple applications including: supporting the surgical team during the intervention, improving the automatic monitoring, designing new teaching paradigms. Methods In this paper, we use low-level recordings of the activities that are performed by a surgeon to automatically predict the current (high-level) phase of the surgery. We augment a decision tree algorithm with the ability to consider the local context of the surgical activities and a hierarchical clustering algorithm. Results Experiments were performed on 22 surgeries of lumbar disk herniation. We obtained an overall precision of 0.843 in detecting phases of 51,489 single activities. We also assess the robustness of the method with regard to noise.
Forestier et al. REF use low-level recordings of the activities that are performed by a surgeon to automatically predict the current phase of the surgery.
Automatic phase prediction from low-level surgical activities
International Journal of Computer Assisted Radiology and Surgery
International Journal of Computer Assisted Radiology and Surgery
Medicine Computer Science