|Description||In the domain of endoscopic surgery, the operating surgeons perform all actions based on images from the inside of the patient, produced by a tiny camera with a light source called the endoscope. Nowadays, these images are typically also recorded and stored in a video archive for later use. Reasons for this are manifold but a very important one is the post-hoc inspection of the video footage for assessing the technical quality of the surgical actions, also known as surgical quality assessment. Through retrospective video review, technical errors are identified and the surgeon is made aware of them, in order to avoid such errors in the future. It is known that this process of managing technical errors in surgery improves patient outcome and increases surgical quality.|
However, currently, the video review is performed manually by an expert assessor, who uses a common video player, a checklist, and some external notes. The problem with this approach is that it is very tedious, inefficient and error-prone, because no supporting software tools are available.
In this research project we aim at improving this inconvenient situation of manual surgical quality assessment. In a joint effort with multimedia experts (from Klagenfurt University) and medical experts (from Medical University of Vienna) we investigate fundamental research questions associated with surgical quality assessment. More precisely, we evaluate deep learning and video retrieval techniques for automatic detection of technical errors in laparoscopic surgery.