Real-time Surgical Tools Recognition in Total Knee Arthroplasty Using Deep Neural Networks

摘要

Total knee arthroplasty (TKA) is a surgical procedure to mitigate knee pain and improve functions for people with knee arthritis. The procedure is complicated due to the different surgical tools used in the stages of surgery. Real-time surgical tool recognition can be used to simplify surgical procedures for the surgeon. Also, the presence and movement of tools in surgery are crucial information for the recognition of the operational phase and to identify the surgical workflow. Therefore, this research proposes a real-time system for recognizing surgical tools using a convolutional neural network (CNN). Surgeons wearing smart glasses can see essential information about tools during surgery that may reduce the complication of the procedures. The performance of the proposed method was evaluated by using mean average precision (MAP) with conventional methods which are fast R-CNN and deformable part models. We achieved 87.6% mAP which is better in comparison to the existing methods. With the additional improvements of our proposed method, it can be a future point of reference, also the baseline for operational phase recognition.