Final result 3m video Final result: 10 minute video ITsAmsterdam
Team Name
ITsAmsterdam
Track
Healthcare
Use Case
Ensuring Patient Safety during a Laparoscopic Cholecystectomy using Deep Learning
Technology
Convoluational Neural Network, Image processing
Region
EMEA
Team lead
Bert Kiewiet
Team members
Alexander Gruter, Bo Klaasse, Timur Durmaz, Ischa van der Molen, Mario Kornuijt, Andre Keizer
Laparoscopic Cholecystectomy (Gallbladder removal, also abbreviated as LapChol) is a surgical procedure done frequently by surgeons all over the world. During the procedure, the surgeons need to gain a clear view of the different structures involved in removing a gallbladder. This view is called the ‘Critical View of Safety’ (CVS). The quality of the view is scored by using the Strasberg criteria. These criteria are widely used in clinical practice
Surgeons in training are coached and rated by qualified surgeons which look at videos in which the surgery is performed. One part of the rating is done by looking at the quality of the CVS. Rating a complete procedure based on a video is a very time-consuming and expensive process.
Our team's objective is to aid in the assessment of videos to determine the quality of the CVS. One very important step in this process is to determine the time when the CVS is best assessable:
First Goal Hackathon:
Develop a computer vision model to automatically find the correct time in a video to assess the Critical View of Safety (CVS).
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