Relevance Detection of Ophthalmic Surgery Videos (OVID)

In this project, we want to investigate fundamental research questions in the field of postoperative analysis of

ophthalmic surgery videos (OSVs). More precisely, three research objectives are covered: (1) Classification of

OSV segments - is it possible to improve upon the state-of-the-art in automatic content classification and content

segmentation of OSVs, focusing on regular and irregular operation phases? (2) Relevance prediction and

relevance-driven compression - how accurately can the relevance of OSV segments be determined automatically

for educational, scientific, and documentary purposes (as medical experts would do), and what compression

efficiency can be achieved for OSVs when considering relevance as an additional modality? (3) Analysis of

common irregularities in OSVs for medical research - we address three quantitative medical research questions

related to cataract surgeries, such as: is there a statistically significant difference in duration or complication rate

between cataract surgeries showing intraoperative pupil reactions and those showing no such pupil reactions?

We plan to perform these investigations using data acquisition, data modelling, video content analysis, statistical

analysis, and state-of-the-art machine learning methods - such as content classifiers based on deep learning.

The proposed methods will be evaluated on annotated video datasets ("ground truth") created by medical field

experts during the project.

Beyond developing novel methods for solving the abovementioned research problems, project results are

expected to have innovative effects in the emerging interdisciplinary field of automatic video-based analysis of

ophthalmic surgeries. In particular, research results of this project will enable efficient permanent video

documentation of ophthalmic surgeries, allowing to create OSV datasets relevant for medical education, training,

and research. Moreover, archives of relevant OSVs will enable novel postoperative analysis methods for medical

research questions - such as causes for irregular operation phases, for example.

The research project will be a cooperation between computer scientists of AAU Klagenfurt (conducted by Prof.

Klaus Schöffmann, supported and advised by Dr. Mario Taschwer and Prof. Laszlo Böszörmenyi) and ophthalmic

surgeons and researchers at Klinikum Klagenfurt (Dr. Doris Putzgruber-Adamitsch, Dr. Stephanie Sarny, Prof.

Yosuf El-Shabrawi).

Schlagworte: Multimedia, Video content analysis, Machine learning, Biomedical engineering, Surgery videos, Computer vision
Kurztitel: OVID
Zeitraum: 01.10.2018 - 30.11.2021
Homepage: -



Projekttyp Forschungsförderung (auf Antrag oder Ausschreibung)
Förderungstyp §26
  • Grundlagenforschung
  • 102020 - Medizinische Informatik (305905)
Forschungscluster Kein Forschungscluster ausgewählt
Genderrelevanz Genderrelevanz nicht ausgewählt
  • Science to Science (Qualitätsindikator: I)
Klassifikationsraster der zugeordneten Organisationseinheiten:
  • Distributed Multimedia Systems


Organisation Adresse
KABEG Klinikum Klagenfurt
Feschnigstraße 11
9020 Klagenfurt
Feschnigstraße 11
AT - 9020  Klagenfurt