  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.
