Learning-based Surgical Workflow Detection from Intra-Operative Signals
This work addresses the need for machine-readable surgical status in operating rooms, but it is incremental as it compares and combines existing methods on a specific procedure.
The paper tackled the problem of automatically detecting surgical workflow phases in laparoscopic cholecystectomy using intra-operative signals, achieving accuracies of 64-72% with Random Forests and 80-82% with a combined Random Forests and Hidden Markov Models approach.
A modern operating room (OR) provides a plethora of advanced medical devices. In order to better facilitate the information offered by them, they need to automatically react to the intra-operative context. To this end, the progress of the surgical workflow must be detected and interpreted, so that the current status can be given in machine-readable form. In this work, Random Forests (RF) and Hidden Markov Models (HMM) are compared and combined to detect the surgical workflow phase of a laparoscopic cholecystectomy. Various combinations of data were tested, from using only raw sensor data to filtered and augmented datasets. Achieved accuracies ranged from 64% to 72% for the RF approach, and from 80% to 82% for the combination of RF and HMM.