Utility of the Simulated Outcomes Following Carotid Artery Laceration Video Data Set for Machine Learning Applications-Reference-Cited by-同舟云学术

Utility of the Simulated Outcomes Following Carotid Artery Laceration Video Data Set for Machine Learning Applications

Published:2022-03-21 Issue:3 Volume:5 Page:e223177
ISSN:2574-3805
Container-title:JAMA Network Open
language:en
Short-container-title:JAMA Netw Open

Author:

Kugener Guillaume¹,Pangal Dhiraj J.¹,Cardinal Tyler¹,Collet Casey¹,Lechtholz-Zey Elizabeth¹,Lasky Sasha¹,Sundaram Shivani¹,Markarian Nicholas¹,Zhu Yichao²,Roshannai Arman¹,Sinha Aditya¹,Han X. Y.³,Papyan Vardan⁴,Hung Andrew⁵,Anandkumar Animashree⁶,Wrobel Bozena⁷,Zada Gabriel¹,Donoho Daniel A.⁸

Affiliation:

1. Department of Neurosurgery, Keck School of Medicine of the University of Southern California, Los Angeles

2. Department of Computer Science, Viterbi School of Engineering, University of Southern California, Los Angeles

3. Department of Operations Research and Information Engineering, Cornell University, Ithaca, New York

4. Department of Mathematics, University of Toronto, Toronto, Ontario, Canada

5. Center for Robotic Simulation and Education, USC Institute of Urology, Keck School of Medicine of the University of Southern California, Los Angeles

6. Department of Computer Science and Mathematics, California Institute of Technology, Pasadena

7. Department of Otolaryngology, Keck School of Medicine of the University of Southern California, Los Angeles

8. Division of Neurosurgery, Center for Neuroscience, Children’s National Hospital, Washington, DC

Publisher

American Medical Association (AMA)

Subject

General Medicine

Link

https://jamanetwork.com/journals/jamanetworkopen/articlepdf/2790172/kugener_2022_oi_220124_1646862767.52045.pdf

Reference57 articles.

1. Video-based surgical coaching: an emerging approach to performance improvement.;Greenberg;JAMA Surg,2016

2. Surgical video in the age of big data.;Hashimoto;Ann Surg,2018

3. Automated performance metrics and machine learning algorithms to measure surgeon performance and anticipate clinical outcomes in robotic surgery.;Hung;JAMA Surg,2018

4. Real-time automatic surgical phase recognition in laparoscopic sigmoidectomy using the convolutional neural network-based deep learning approach.;Kitaguchi;Surg Endosc,2020

5. Development and validation of a 3-dimensional convolutional neural network for automatic surgical skill assessment based on spatiotemporal video analysis.;Kitaguchi;JAMA Netw Open,2021

Cited by 9 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Simulated outcomes for durotomy repair in minimally invasive spine surgery;Scientific Data;2024-01-10

2. Pilot Analysis of Surgeon Instrument Utilization Signatures Based on Shannon Entropy and Deep Learning for Surgeon Performance Assessment in a Cadaveric Carotid Artery Injury Control Simulation;Operative Neurosurgery;2023-09-01

3. In Reply: Deep Neural Networks Can Accurately Detect Blood Loss and Hemorrhage Control Task Success from Video;Neurosurgery;2023-06-30

4. Neurosurgical skills analysis by machine learning models: systematic review;Neurosurgical Review;2023-05-16

5. Extraction of SIFT Features for Identifying Disaster Hit areas using Machine Learning Techniques;2022 International Conference on Knowledge Engineering and Communication Systems (ICKES);2022-12-28