CataNet: Predicting remaining cataract surgery duration

Marafioti, Andrés; Hayoz, Michel; Gallardo, Mathias; Márquez Neila, Pablo; Wolf, Sebastian; Zinkernagel, Martin; Sznitman, Raphael (September 2021). CataNet: Predicting remaining cataract surgery duration. In: MICCAI 2021, 24th International Conference on Medical Image Computing and Computer Assisted Intervention. Lecture Notes in Computer Science: Vol. 12904 (pp. 426-435). Springer 10.1007/978-3-030-87202-1_41

	Text MICCAI_2021___Cataract.pdf - Accepted Version Restricted to registered users only Available under License Publisher holds Copyright. Download (601kB) | Request a copy
	Text Marafioti2021_Chapter_CataNetPredictingRemainingCata.pdf - Published Version Restricted to registered users only Available under License Publisher holds Copyright. Download (1MB) | Request a copy

Cataract surgery is a sight saving surgery that is performed over 10 million times each year around the world. With such a large demand, the ability to organize surgical wards and operating rooms efficiently is critical to delivery this therapy in routine clinical care. In this context, estimating the remaining surgical duration (RSD) during procedures is one way to help streamline patient throughput and workflows. To this end, we propose CataNet, a method for cataract surgeries that predicts in real time the RSD jointly with two influential elements: the surgeon's experience, and the current phase of the surgery. We compare CataNet to state-of-the-art RSD estimation methods, showing that it outperforms them even when phase and experience are not considered. We investigate this improvement and show that a significant contributor is the way we integrate the elapsed time into CataNet's feature extractor.

Interest & Impact

Downloads

5 since deposited on 29 Jun 2021
0 in the past 12 months

Citations

5 Citations in Web of Science ®
6 Citations in Scopus

Search

in Google Scholar™

Services

Actions (login required)

Edit item

Actions (login required)

Edit item