Surgical Quality Assessment in Gynecologic Laparoscopy

Endoscopic surgeries require specific psychomotor skills that are difficult to learn and teach, and typicallyresult in prolonged learning curves. These psychomotor skills have direct impact on the performance of the surgery, especially in a field with complex operation techniques. In order to assess surgical quality objectively, medical experts currently record the entire surgery on video and inspect and analyze the unedited video footage in a post-operative session for the occurrence of technical errors, according to some standardized rating scheme. Several studies have shown that such post-operative analysis of errors and the reporting of them to the responsible surgeons can significantly improve their performance over time and lead to better surgical quality, especially for young surgeons. However, currently the surgical quality assessment (SQA) process is so tedious and time-consuming that many surgeons/clinicians cannot afford to perform such error ratings, which is very unfortunate since their application would improve surgical quality and patient outcome. The main reason for the high effort is the fact that it is performed without any special errorrating software, but with common software video players and manually edited checklists, where surgeons enter timestamps of corresponding relevant scenes in the video. This renders SQA currently not only a very time-consuming process, but also a very error-prone one.

In this research project we want to address this issue and find out how we can make surgical quality assessment (SQA) more efficient through automatic video content analysis and, hence, more feasible. More specifically, for the field of gynecologic laparoscopy we want to investigate to what extent current methods of machine learning and content-based video retrieval can support SQA (i.e., optimize the entire process through automatic classification and retrieval of technical errors). For that purpose, we will evaluate deep learning approaches as well as video content description and similarity search.

We consider this research project as a pioneering work in the interdisciplinary overlap of computer- and medical science, which will investigate fundamental research questions that should provide the basis for future computer-aided SQA. We expect that our research results will help to significantly facilitate the currently cumbersome and error-prone SQA process, and hence enable more surgeons to actually perform error ratings. We expect this project even to contribute to improve surgical education in the long run (through higher penetration of the SQA process – due to lower time effort), and thereby raise surgical quality itself. Project results and their later application in appropriate software tools could help surgeons to keep track of their surgical actions in a novel, highly efficient way, and thus help them to avoid technical errors. This will not only save valuable time of medical experts and increase the performance of quality assessment, but also contribute to surgical risk management and quality control.

Schlagworte: multimedia, video content analysis, video retrieval, machine learning, biomedical engineering, computer vision
Kurztitel: SQUASH
Zeitraum: 01.04.2019 - 31.03.2023



Projekttyp Forschungsförderung (auf Antrag oder Ausschreibung)
Förderungstyp §26
  • Grundlagenforschung
  • 102003 - Bildverarbeitung
  • 102004 - Bioinformatik (106005)
  • 102019 - Machine Learning
Forschungscluster Kein Forschungscluster ausgewählt
Genderrelevanz Genderrelevanz nicht ausgewählt
  • Science to Science (Qualitätsindikator: I)
Klassifikationsraster der zugeordneten Organisationseinheiten:
Arbeitsgruppen Keine Arbeitsgruppe ausgewählt


Organisation Adresse
St Michael’s Hospital
30 Bond St.
M5B1W8 Toronto
30 Bond St.
CA - M5B1W8  Toronto
Medizinische Universität Wien
Spitalgasse 23
1090 Wien
Österreich - Wien
Spitalgasse 23
AT - 1090  Wien
Surgical Safety Technologies Inc.
209 Victoria Street
M5B1T8 Toronto
209 Victoria Street
CA - M5B1T8  Toronto