|Relevance Detection in Ophthalmic Surgery Videos
|October 2018 – September 2022
|In an interdisciplinary research project, computer scientists and physicians collaborate to develop and evaluate methods for automatic detection of relevant temporal segments in ophthalmic surgery 1 videos (OSVs). The main objective is the creation of relevance models allowing to detect video segments that are relevant for educational, scientific, or documentary purposes in medicine. Relevance models are produced by machine learning algorithms, which are trained using OSVs that were annotated by surgeons. Important instances of relevant OSV segments are irregular operation (OP) phases, which deviate from the usual procedure used in quasi-standardized ophthalmic surgeries. The automatic detection and classification of irregularities that occur more frequently and hence allow training of machine learning algorithms, provide an additional benefit for the creation of OSV datasets targeted at medical education or research. The development and evaluation of automatic classifiers of irregularities therefore represents another research objective of this project. Relevance models can be used to compress and store OSVs efficiently. This project will develop and evaluate appropriate methods and algorithms to achieve this goal. Finally, we want to demonstrate that relevant OSV segments are useful for medical research by addressing three specific medical research questions related to a certain type of ophthalmic surgery (cataract) using video analysis.
|Assoc.-Prof. DI Dr. Klaus Schöffmann
|Mag. Dr. Mario Taschwer; Natalia Sokolova, MSc; Negin Ghamsarian, MSc; DI Markus Fox; Lisa Bürger
|Klinikum Klagenfurt (KABEG)
|Sponsored or supported by
|Fonds zur Förderung der wissenschaftlichen Forschung (FWF)