Surgical Phase Recognition Method with a Sequential Consistency for CAOS-AI Navigation System

The procedure of orthopedic surgery is quite complicated, and many kinds of equipment have been used. Operating room nurses who deliver surgical instruments to surgeon are supposed to be forced to incur a heavy burden. There are some studies to recognize surgical phase with convolutional neural network (CNN) in minimally invasive laparoscopic surgery only. Previously, we proposed a computer-aided orthopedic surgery (CAOS)-AI navigation system based on CNN. However, the work propose a method to improve accuracy of phase recognition by considering temporal dependency of orthopedic surgery video acquired from surgeon-wearable video camera. The method estimates current surgical phase by combining both temporal dependency and convolutional-long-short term memory network (CNN-LSTM). Experimental results shows a phase recognition accuracy of 59.9% by the proposed method applied in unicomapartmenatal knee arthroplasty (UKA).