ARAS-Farabi Experimental Framework for Skill Assessment in Capsulorhexis Surgery

Abstract

Automatic surgical instruments detection in recorded videos is a key component of surgical skill assessment and content-based video analysis. Such analysis may be used to develop training techniques, especially in ophthalmology. This research focuses on capsulorhexis, the most fateful process in cataract surgery, which is a very delicate procedure and requires very high surgical skill. Assessment of the surgeon's skill in handling surgical instruments is one of the main parameters of surgical quality assessment, and requires the proper detection of important instruments and tissues during a surgical procedure. The traditional methods to accomplish this task are very time-consuming and effortful, and therefore, automating this process by using computer vision approaches is a stringent requirement. In order to accomplish this requirement, a proper dataset is prepared. By consulting the expert surgeons, the pupil, and the surgical tool, namely the capsulorhexis cystotome, are annotated in this dataset. Then, we created a general framework for implementing and examining different approaches, models, and techniques on the developed dataset, and reporting the comparative analysis. This study shows that the object detection task can be accurately performed for various scenarios using this dataset. The developed dataset, the developed deep learning general framework, and other developments are made public for further research. © 2021 IEEE.