Automated Detection and Classification of Adverse Events in Surgery

During surgeries, it is important to keep track of what is happening with the patient, the steps being taken during the surgery by the operating staff, and unforeseen events that occur. All the previous correspond to the surgical workflow. Keeping track of the workflow is essential to achieving a better and safer surgery. In the past, computational tools have been developed to track each step the surgeon takes during the surgery, and dividing the separate surgical phases. However, the adverse events have not been tracked. This project aims to develop computer software capable of detecting adverse events that occur during surgery and classifying their severity. This way, it is expected to assess surgical performance at a faster pace. The advances that are achieved from this internship will improve surgical skills, improve patient outcomes and reduce unnecessary healthcare costs.

Faculty Supervisor:

Babak Taati

Student:

Juliana De La Vega Fernandez

Partner:

Surgical Safety Technologies Inc

Discipline:

Computer science

Sector:

Medical devices

University:

Program: