Surgical Phase Recognition for different hospitals

Abstract

Abstract Surgical phase recognition is an important aspect of surgical workflow analysis, as it allows an automatic analysis of the performance and efficiency of surgical procedures. A big challenge for training a neural network for surgical phase recognition is the availability of training data and the large (visual) variability in procedures of different surgeons. Hence, a network must be able to generalize to new data. In this paper, we present an adaptation of a Temporal Convolutional Network for surgical phase recognition in order to ensure the generalization of the network to new scenes with different conditions on the example of cholecystectomy. We used publicly available datasets of 104 surgeries from four different centers for training. The results showed that the network was able to generalize to new scenes and we obtained recognition results with accuracy up to 82% on our own six captured surgeries, performed in a different hospital. This performance is similar for test data from the hospitals of the training data, suggesting that the network can well generalize to new surgical rooms and surgeons. The findings have important implications for the development of automated surgical decision support systems that can be applied in a variety of real-world surgical settings.