A Deep Learning Approach for Surgical Instruments Detection System in Total Knee Arthroplasty—Automatic Creation of Training Data and Reduction of Training Time—

Abstract

In total knee arthroplasty (TKA), many surgical instruments are available. Many of these surgical instruments are similar in shape and size. For this reason, there have been accidents due to incorrect selection of implants. Furthermore, a shortage of nurses is expected worldwide. There will also be a shortage of scrub nurses, which will result in an increased burden on each scrub nurse. For these reasons, we have developed a surgical instrument detection system for TKA to reduce the burden on scrub nurses and the number of accidents, such as implant selection errors. This study also focuses on automating the acquisition of data for training. We also develop a method to reduce the additional training time when the number of detection targets increases. In this study, YOLOv5 is used as the object detection method. In experiments, we examine the accuracy of the training data automatically acquired and the accuracy of object detection for surgical instruments. In object detection, several training files are created and compared. The results show that the training data is sufficiently effective, and high accuracy is obtained in object detection. Object detection is performed in several cases, and one of the results shows an IoU of 0.865 and an F-measure of 0.930.