Surgical Safety Technology aims to improve operating room safety by capturing and analyzing operation videos. Usually, operating room traffic (like people displacement) has a huge impact on surgery. Unnecessary movements can cause distraction of surgeons and pollution of the sterile environment. This project applies computer vision models to detect and track people movements in the operating room and assesses the relationship between adverse events and errors. Popular machine learning models such as Deep Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) have the capability to analyze time sequential data. Trained on the well-labeled data directly from specific hospitals, these models could work out precise operating room traffic trace and its correlation with surgical events.
Surgical Safety Technologies Inc
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