TY - GEN
T1 - Construction of Quantitative Indexes for Cataract Surgery Evaluation Based on Deep Learning
AU - Gu, Yuanyuan
AU - Hu, Yan
AU - Mou, Lei
AU - Hao, Hua Ying
AU - Zhao, Yitian
AU - Zheng, Ce
AU - Liu, Jiang
N1 - Publisher Copyright:
© 2020, Springer Nature Switzerland AG.
PY - 2020
Y1 - 2020
N2 - Objective and accurate evaluation of cataract surgery is a necessary way to improve the operative level of resident and shorten the learning curve. Our objective in this study is to construct quantifiable evaluation indicators through deep learning techniques to assist experts in the implementation of evaluation and verify the reliability of the evaluation indicators. We use a data set of 98 videos of incision, which is a critical step in cataract surgery. According to the visual characteristics of incision evaluation indicators specified in the International Council of Ophthalmology’s Ophthalmology Surgical Competency Assessment Rubric: phacoemulsification (ICO-OSCAR: phaco), we propose using the ResNet and ResUnet to obtain the keratome tip position and the pupil shape to construct the quantifiable evaluation indexes, such as the tool trajectory, the size and shape of incision, and the scaling of a pupil. Referring to the motion of microscope and eye movement caused by keratome pushing during the video recording, we use the center of the pupil as a reference point to calculate the exact relative motion trajectory of the surgical instrument and the incision size, which can be used to directly evaluate surgical skill. The experiment shows that the evaluation indexes we constructed have high accuracy, which is highly consistent with the evaluation of the expert surgeons group.
AB - Objective and accurate evaluation of cataract surgery is a necessary way to improve the operative level of resident and shorten the learning curve. Our objective in this study is to construct quantifiable evaluation indicators through deep learning techniques to assist experts in the implementation of evaluation and verify the reliability of the evaluation indicators. We use a data set of 98 videos of incision, which is a critical step in cataract surgery. According to the visual characteristics of incision evaluation indicators specified in the International Council of Ophthalmology’s Ophthalmology Surgical Competency Assessment Rubric: phacoemulsification (ICO-OSCAR: phaco), we propose using the ResNet and ResUnet to obtain the keratome tip position and the pupil shape to construct the quantifiable evaluation indexes, such as the tool trajectory, the size and shape of incision, and the scaling of a pupil. Referring to the motion of microscope and eye movement caused by keratome pushing during the video recording, we use the center of the pupil as a reference point to calculate the exact relative motion trajectory of the surgical instrument and the incision size, which can be used to directly evaluate surgical skill. The experiment shows that the evaluation indexes we constructed have high accuracy, which is highly consistent with the evaluation of the expert surgeons group.
KW - Cataract surgery assessment
KW - Deep learning
UR - http://www.scopus.com/inward/record.url?scp=85097419713&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-63419-3_20
DO - 10.1007/978-3-030-63419-3_20
M3 - Conference contribution
AN - SCOPUS:85097419713
SN - 9783030634186
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 195
EP - 205
BT - Ophthalmic Medical Image Analysis - 7th International Workshop, OMIA 2020, Held in Conjunction with MICCAI 2020, Proceedings
A2 - Fu, Huazhu
A2 - Garvin, Mona K.
A2 - MacGillivray, Tom
A2 - Xu, Yanwu
A2 - Zheng, Yalin
PB - Springer Science and Business Media Deutschland GmbH
T2 - 6th International Workshop on Ophthalmic Medical Image Analysis, OMIA 2020, held in conjunction with 23rd International Conference on Medical Imaging and Computer-Assisted Intervention, MICCAI 2020
Y2 - 8 October 2020 through 8 October 2020
ER -