# Development of a Deep Learning-Based Algorithm to Detect the Distal End of a Surgical Instrument
## 4 Discussion
There are many types of surgical instruments, such as monopolar and bipolar handpieces, biological tweezers, forceps, surgical scissors and scalpels. Furthermore, the evaluation of a surgical instrument detection method has been reported [23], with many procedures and datasets. Therefore, depending on the various types of operations and techniques, a direct comparison of surgical instruments, under the same conditions, should be considered in future work. Second, the present work only focused on the scene where carotid arteries were exposed. The primary reason for focusing on this scene was to evaluate a delicate operative technique with inherent risks, such as cranial nerve injury, the possibility of ischemic stroke caused by plaque disruption, and hemorrhage. This scene was known to involve the most technical surgical performances; that is, those during a CEA [40]. Third, with regard to the conversion from surgical records to JPEG files, the training data in this work used a 30 fps temporal resolution for all conversions. In this work, the scene of the dataset was selected in order to focus on detecting the feature of the instruments via the time resolution, rather than letting the amount of features obtained depend on the length of time. Reducing the frame rate for JPEG conversion would provide more image features; however, the repeating pattern of similar movements might not be affected by the improvement associated with obtaining new image features. The fps of the conversion was adequate for the given procedure, because the approach to the blood vessels was a particularly important part of the surgery, involving the potential risk of bleeding. Nevertheless, this approach contributed to these novel findings and insights in the field of the evaluation of the method of detecting the distal end of the surgical instrument. Note that previous works [41; 42; 43] have reported the detection of the point of the joint for animals and human body parts. However, the approach reported in these works required a dedicated technique to train the dataset. Moreover, many works [35; 36; 44; 45] have reported the detection of entire surgical instruments using semantic segmentation. These works were used in the Endoscopic Vision Challenge ([[http://endovis.grand-challenge.org](http://endovis.grand-challenge.org)]([http://endovis.grand-challenge.org](http://endovis.grand-challenge.org))) dataset at the international conference on Medical Image Computing and Computer Assisted Intervention. To the best of our knowledge, detection of the distal end of a surgical instrument, with a particular focus on CEA operations, has not been reported, and this we have done, even though our work was based on a currently limited dataset. The specific target surgery uses similar techniques and instruments, even though the operations have a variety of purposes and employ a variety of techniques. As with all common procedures, the instruments were almost the same for all operations. In particular, the scene of the approach to the vein focuses on the vessels and the instruments. Therefore, the detection of the distal end of the surgical instrument with deep learning will be useful for common surgical procedures.