CataNet: Predicting remaining cataract surgery duration

TL;DR

CataNet is a real-time prediction system for remaining cataract surgery duration that also considers surgeon experience and surgical phase to improve efficiency in clinical settings.

Contribution

It introduces CataNet, a novel method that jointly predicts remaining surgery time, surgeon experience, and surgical phase, outperforming existing RSD estimation techniques.

Findings

CataNet outperforms state-of-the-art RSD methods.

Integration of elapsed time significantly improves prediction accuracy.

Considering surgeon experience and surgical phase enhances model performance.

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.