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.
翻译:白内障手术是全世界每年超过1000万次的挽救视力手术,由于需求如此之大,组织手术病房和手术室的能力对于在常规临床护理中提供这种治疗至关重要。在这方面,估计手术过程中的剩余外科手术期限(RSD)是帮助简化病人吞吐量和工作流程的一种方法。为此,我们提议CataNet,这是一种白内障手术方法,它实时地结合两个有影响力的因素来预测RCD:外科医生的经验和手术的目前阶段。我们将CataNet与最先进的RSD估计方法相比较,表明即使在不考虑阶段和经验的情况下,它也优于这些方法。我们调查这一改进,并表明一个重要的贡献者是我们将过去的时间融入CataNet的特征提取器。