# Development of a Deep Learning-Based Algorithm to Detect the Distal End of a Surgical Instrument
## 4 Discussion  
The purpose of this work was to develop and evaluate an algorithm to detect the distal end of a surgical instrument, using object detection with deep learning, during surgery. With regard to detection as a bounding box, the AP with data augmentation was \(0.7718\pm 0.0824\). This result confirmed that the data augmentation was able to achieve less dependency on location and angle of the distal end of surgical instruments, even though such instruments have a variety of types and shapes. We set the rotation angle of the data augmentation to \(-90^{\circ}\)-\(90^{\circ}\), in \(5^{\circ}\) steps, because the view of the surgical field was commonly recorded with reference to the view of the main surgeon. Approximately 80% of the AP was appropriate for comparing our results with those of another report [9] on the detection of small anatomies of the brain with deep learning. The FPS with data augmentation showed a slight variation, because the surgical instrument's motion was relatively different throughout the images of the test data, depending on the surgical procedure of that scene. The relationships between the position or motion of the detectable object, and the precision or FPS of the detection, have been reported [33; 34], even though the entire surgical instrument's motions through the test data were not evaluated. With regard to the proposed algorithm for the detection of the distal end of the surgical instrument, the detection rates for the center point of the \(8\times 8\) and \(16\times 16\) pixels were \(0.6100\pm 0.1014\) and \(0.9653\pm 0.0177\), respectively, even though the evaluated condition was limited to valid bounding boxes. Similarly to previous results for the assessment of surgical skill level in the surgical operation [18; 19], our results \begin{table}
\begin{tabular}{c c c} \hline \hline  & **Detection Rate of the Distal End of the Surgical Instrument** \\  & **Within the Center of \(8\times 8\) Pixels** & **Within the Center of \(16\times 16\) Pixels** \\ \hline Subset A & 0.5388 & 0.9660 \\ Subset B & 0.4916 & 0.9241 \\ Subset C & 0.5826 & 0.9604 \\ Subset D & 0.5033 & 0.9560 \\ Subset E & 0.6430 & 0.9706 \\ Subset F & 0.6742 & 0.9796 \\ Subset G & 0.8210 & 0.9828 \\ Subset H & 0.6419 & 0.9733 \\ Subset I & 0.5934 & 0.9750 \\ \hline mean \(\pm\) SD & \(0.6100\pm 0.1014\) & \(0.9653\pm 0.0177\) \\ \hline \hline \end{tabular}
\end{table} 
Table 2: The detection rate of the distal end of the surgical instrument. Figure 7: Representative examples of the bounding box and surgical instrument distal end detection results. indicate that the proposed algorithm is efficient as an indicator for the analysis of a surgical operation. The proposed algorithm for the detection of the distal end of the surgical instrument as a point differed procedurally from those in previous reports [35; 36], in which the proposed method was calculated using the center coordinates of the bounding box. CNN architectures for object detection have been widely used with many deep learning frameworks [37; 38; 39; 32]. The advantage of this algorithm is that is creates the dataset using the existing YOLOv2 [32]. Specifically, the only attempt to define the square size of the ROIs around the distal end of the surgical instruments that was easy was that which did not require the information of the whole instrument, because the surgical instrument states varied in the operation fields of the video record. For the distal end detection, the calculation of the coordinates in the image that depicted the distal end of the surgical instruments was not necessary to complete the calculation, because the proposed bounding boxes always showed as a square shape. The limitations of the present work are as follows. First, the scenes of the surgical record were limited to CEAs. Although the surgical procedures were performed in many parts of the body, in a preliminary study, it was necessary to focus on a single procedure.