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.