Intra-operative Error Detection on Surgical Video based on Computer Vision Analysis

The intra-operative errors that occurs in adverse events have been a major concern in healthcare and surgical industry. Conventionally, error-event assessment is done by peer surgeon review, which is time consuming and costly. With the advances in machine learning and computer vision techniques, it is possible to keep track of the operation surgical procedures based on recorded surgical videos to evaluate and classify the errors occurred. With the proposed computer vision-based algorithm, it is expected to predict the error event during surgery in a scalable process to ensure a better and safer patient and surgical environment.
Since the intra-operative error detection algorithm is mainly trained on recorded surgical video data, it is expected to have an impact on improving decision-making and performance in future operations for complex patients and surgical circumstances.

Faculty Supervisor:

Sanja Fidler


Yichen Zhang


Surgical Safety Technologies Inc


Computer science


Medical devices




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