Automation Data Acquisition and Shortening Training Time for Surgical Instrument Detection System in Total Knee Arthroplasty

In total knee arthroplasty, nearly 100 types of surgical instruments are prepared in addition to the usual surgical instruments. Since there are many types of surgical instruments used and their shapes and sizes are similar, accidents due to incorrect selection of implants have occurred. In addition, there is concern that the shortage of nurses will accelerate around the world in the future. For these reasons, we developed a surgical instrument detection system for total knee arthroplasty to reduce the incidence of accidents and the burden on scrub nurses. The surgical instrument detection system in the previous study required a lot of labor and time to create training data. Also, when increasing the number of surgical instruments to be detected, it was necessary to retrain all the surgical instruments. To solve these problems, we developed an automatic annotation system using a turntable in this study. In addition, we develop learning and inference methods using standard objects. In the experiment, we verified the accuracy of the learning data created by the automatic annotation system and verified the object detection accuracy for five types of surgical instruments. As a result, all experiments showed the effectiveness of this system.