# Development of a Deep Learning-Based Algorithm to Detect the Distal End of a Surgical Instrument
###### Abstract  
This work aims to develop an algorithm to detect the distal end of a surgical instrument using object detection with deep learning. We employed nine video recordings of carotid endarterectomies for training and testing. We obtained regions of interest (ROI; \(32\times 32\) pixels), at the end of the surgical instrument on the video images, as supervised data. We applied data augmentation to these ROIs. We employed a You Only Look Once Version 2 (YOLOv2) -based convolutional neural network as the network model for training. The detectors were validated to evaluate average detection precision. The proposed algorithm used the central coordinates of the bounding boxes predicted by YOLOv2. Using the test data, we calculated the detection rate. The average precision (AP) for the ROIs, without data augmentation, was \(0.4272\pm 0.108\). The AP with data augmentation, of \(0.7718\pm 0.0824\), was significantly higher than that without data augmentation. The detection rates, including the calculated coordinates of the center points in the centers of \(8\times 8\) pixels and \(16\times 16\) pixels, were \(0.6100\pm 0.1014\) and \(0.9653\pm 0.0177\), respectively. We expect that the proposed algorithm will be efficient for the analysis of surgical records.  
object detection; deep learning; surgical video analysis +
Footnote †: journal: Medical  
specialties, because these surgeries require special surgical finesse and instruments, in which robotic and endoscopic means could not have been employed [24].  
We previously reported \"tissue motion\" measurement results, as representative of the \"gentle\" handling of tissue during exposure of the carotid artery in a carotid endarterectomy (CEA). Therein, we employed off-the-shelf software for the video-based analysis [25]. However, we performed the analysis manually or semi-automatically; therefore, the analysis process was only retrospective, and quite time-consuming. Consequently, we could not realize real-time analysis. Based on these issues, we have introduced a deep learning technique for the analysis, because it is expected to realize automated analysis for real-time monitoring and feedback. Besides, for the analysis of surgical performance, a deep learning technique is expected to be more objective [18; 19].  
In this work, we focus on surgical instrument motion tracking during a CEA, as motion tracking information could be utilized to avoid surgical risk (e.g., unexpected tissue injury), as well as the objective assessment of surgical performance. For this purpose, we target the distal end of a surgical instrument, as this tip is the most important site that directly interacts with patient tissue (i.e., dissection, coagulation and grasping of the tissues) and surgical materials (i.e., holding of needles and cotton). Although there have been some studies that show the procedures for the detection of the shape or certain parts of surgical instruments (i.e., pose estimation of instruments) [21; 22; 23; 24; 26], we focus on the detection of the distal end as a goal in this work. This work aims to develop an algorithm to detect the distal end of a surgical instrument, using object detection with deep learning. We anticipate that the proposed algorithm can be used as an object detection technique with deep learning.