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

TL;DR

This paper presents a real-time deep learning system for recognizing surgical tools during total knee arthroplasty, aiding surgeons with instant information and potentially improving surgical outcomes.

Contribution

It introduces a CNN-based system for real-time surgical tool recognition that outperforms existing methods like fast R-CNN and DPM in accuracy.

Findings

Achieved 87.6% mean Average Precision (mAP) in tool recognition.

Outperformed state-of-the-art methods in accuracy.

Provides a baseline for future operational phase recognition in surgery.

Abstract

Total knee arthroplasty (TKA) is a commonly performed 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. The recognition of surgical tools in real-time can be a solution 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 the development of a real-time system for the recognition of surgical tools during surgery 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. To evaluate the performance of the proposed method, we calculated and compared the Mean Average Precision (MAP) with state-of-the-art methods which are fast R-CNN and deformable part models (DPM). 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.