Deep Video Analysis Methods for Surgical Skills Assessment in Cataract Surgery

It is important for a graduate surgical trainee in ophthalmology to have a strong understanding of how to proficiently perform cataract surgery. The surgical training curriculum should incorporate methodical assessments of surgical skills and improve trainee surgeons expertise to maintain patient safety. Prior rating scales for cataract surgery are highly dependant on the subjective opinion of the observing grader and are time consuming. This project is intended to develop a deep learning model for skill evaluation in cataract surgery using raw surgery videos that can supplement human review. An advanced convolutional neural network model is leveraged in this work and was evaluated using a large custom dataset. Videos from four phases in cataract surgery were used to quantify the model performance. Our model yielded an average accuracy of 82% for all four phases of cataract surgery.

Name thesis:degree_name

Master of Computer Science (M.C.S.)

Level thesis:degree_level

Master's

Discipline thesis:degree_discipline

Computer Science

Grantor dc:publisher

Carleton University

Year dc:date.issued

2022

Author dc:creator

• Tanin, Ummey Hani

Statement dc:rights

• Copyright © 2022 the author(s). Theses may be used for non-commercial research, educational, or related academic purposes only. Such uses include personal study, research, scholarship, and teaching. Theses may only be shared by linking to Carleton University Institutional Repository and no part may be used without proper attribution to the author. No part may be used for commercial purposes directly or indirectly via a for-profit platform; no adaptation or derivative works are permitted without consent from the copyright owner.

Language dc:language.iso

en

DOI

10.22215/etd/2022-15129

OAI identifier oai:identifier

oai:carleton.scholaris.ca:20.500.14718/42713