Analyzing Before Solving: Which Parameters Influence Low-Level Surgical Activity Recognition
Olga Dergachyova, Xavier Morandi, Pierre Jannin

TL;DR
This study identifies key sensors for low-level surgical activity recognition, showing that combining anatomical structure recognition with either instrument or verb signals suffices, informing future smart operating room designs.
Contribution
The paper demonstrates that recognizing anatomical structures along with either instruments or actions is sufficient for accurate activity recognition, reducing sensor requirements.
Findings
Instrument and verb signals are redundant for activity recognition.
Recognizing anatomical structures is crucial for accurate identification.
Combining structure, instrument, and historical context improves robustness under noise.
Abstract
Automatic low-level surgical activity recognition is today well-known technical bottleneck for smart and situation-aware assistance for the operating room of the future. Our study sought to discover which sensors and signals could facilitate this recognition. Low-level surgical activity represents semantic information about a surgical procedure that is usually expressed by the following elements: an action verb, surgical instrument, and operated anatomical structure. We hypothesized that activity recognition does not require sensors for all three elements. We conducted a large-scale study using deep learning on semantic data from 154 operations from four different surgeries. The results demonstrated that the instrument and verb encode similar information, meaning only one needs to be tracked, preferably the instrument. The anatomical structure, however, provides some unique cues, and it…
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Taxonomy
TopicsSurgical Simulation and Training · Cardiac, Anesthesia and Surgical Outcomes · Simulation-Based Education in Healthcare
