Cholecystectomy Surgical Instrument Detection using Variants of YOLOv8

Advancements in object detection technologies have ushered in a new era of precision and efficiency in Computer Aided Laparoscopy (CAL), offering transformative benefits to surgical interventions. The importance of adopting and integrating state-of-the-art object detection methodologies within CAL systems is quite critical. Modern object detection algorithms enable CAL systems to accurately localize and track surgical instruments in real-time laparoscopic video feeds. These advancements empower surgeons with enhanced visualization, improved navigation within anatomical structures, and precise instrument guidance during procedures. Furthermore, the seamless integration of object detection technologies with CAL facilitates automated tool recognition, reduces surgical errors, and enhances surgical outcomes. By harnessing the capabilities of deep learning, Convolutional Neural Networks (CNNs), and computer vision techniques, CAL systems can optimize surgical workflow, increase patient safety, and foster a new paradigm of data-driven surgical precision. Recently version 8 of the famous You Look Only Once (YOLO) algorithm was released. Like all the past releases, it has been claimed that this version is better at detection accuracy as well as computational efficiency. This paper examines the performance of YOLOv8 at tool detection in a CAL context. We employed a well-known laparoscopy tool detection benchmark dataset in this research. Models with superior performance have been obtained as a result of this research. Models are superior in terms of both detection accuracy as well as inference speed. Moreover, the models are ready to be deployed into a production environment. The results that are reported in this paper are not only useful for the surgical community but also for the benchmarking of the YOLO algorithm.