Using object detection for surgical tool recognition in simulated open inguinal hernia repair surgery

Abstract

Following the shift from time-based medical education to a competency-based approach, a computer-assisted training platform would help relieve some of the new time burden placed on physicians. A vital component of these platforms is the computation of competency metrics which are based on surgical tool motion. Recognizing the class and motion of surgical tools is one step in the development of a training platform. Object detection can achieve tool recognition. While previous literature has reported on tool recognition in minimally invasive surgeries, open surgeries have not received the same attention. Open Inguinal Hernia Repair (OIHR), a common surgery that general surgery residents must learn, is an example of such surgeries. We present a method for object detection to recognize surgical tools in simulated OIHR. Images were extracted from six video recordings of OIHR performed on phantoms. Tools were labelled with bounding boxes. A YOLOV3 object-detection model was trained to recognize the tools used in OIHR. The Average Precision scores per class and the mean Average Precision (mAP) were reported to benchmark the model’s performance. The mAP of the tool classes was 0.61, with individual Average Precision scores reaching up to 0.98. Tools with poor visibility or similar shapes such as the forceps, or scissors achieved lower precision scores. With an object detection network that can identify tools, research can be done on tissue-tool interactions to achieve workflow recognition. Workflow recognition would allow a training platform to detect the tasks performed in hernia repair surgeries.