%0 Journal Article %1 marafioti2021catanet %A Marafioti, Andrés %A Hayoz, Michel %A Gallardo, Mathias %A Neila, Pablo Márquez %A Wolf, Sebastian %A Zinkernagel, Martin %A Sznitman, Raphael %D 2021 %J MICCAI 2021 %K cataract duration learning machine prediction surgery %T CataNet: Predicting remaining cataract surgery duration %U http://arxiv.org/abs/2106.11048 %X 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.

@article{marafioti2021catanet, abstract = {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.}, added-at = {2021-08-08T00:02:03.000+0200}, author = {Marafioti, Andrés and Hayoz, Michel and Gallardo, Mathias and Neila, Pablo Márquez and Wolf, Sebastian and Zinkernagel, Martin and Sznitman, Raphael}, biburl = {https://puma.ub.uni-stuttgart.de/bibtex/2c8cf5754f9fad233aa578b94d9415acb/felixholm}, description = {[2106.11048] CataNet: Predicting remaining cataract surgery duration}, interhash = {54ef809d5ce310b36f6bfc9ac82caceb}, intrahash = {c8cf5754f9fad233aa578b94d9415acb}, journal = {MICCAI 2021}, keywords = {cataract duration learning machine prediction surgery}, note = {cite arxiv:2106.11048Comment: Accepted at MICCAI 2021}, timestamp = {2021-08-07T22:23:50.000+0200}, title = {CataNet: Predicting remaining cataract surgery duration}, url = {http://arxiv.org/abs/2106.11048}, year = 2021 }