Computer vision in the surgical operating room
Article
Chadebecq, F., Vasconcelos, F., Mazomenos, E. and Stoyanov, D. 2020. Computer vision in the surgical operating room. Visceral Medicine. 36 (6), pp. 456-462. https://doi.org/10.1159/000511934
Type | Article |
---|---|
Title | Computer vision in the surgical operating room |
Authors | Chadebecq, F., Vasconcelos, F., Mazomenos, E. and Stoyanov, D. |
Abstract | Background: Multiple types of surgical cameras are used in modern surgical practice and provide a rich visual signal that is used by surgeons to visualize the clinical site and make clinical decisions. This signal can also be used by artificial intelligence (AI) methods to provide support in identifying instruments, structures, or activities both in real-time during procedures and postoperatively for analytics and understanding of surgical processes. Summary: In this paper, we provide a succinct perspective on the use of AI and especially computer vision to power solutions for the surgical operating room (OR). The synergy between data availability and technical advances in computational power and AI methodology has led to rapid developments in the field and promising advances. Key Messages: With the increasing availability of surgical video sources and the convergence of technologies around video storage, processing, and understanding, we believe clinical solutions and products leveraging vision are going to become an important component of modern surgical capabilities. However, both technical and clinical challenges remain to be overcome to efficiently make use of vision-based approaches into the clinic. |
Keywords | Artificial intelligence; Computer-assisted intervention; Computer vision; Minimally invasive surgery |
Publisher | Karger |
Journal | Visceral Medicine |
ISSN | 2297-4725 |
Electronic | 2297-475X |
Publication dates | |
Online | 15 Oct 2020 |
Publication process dates | |
Submitted | 12 Jun 2020 |
Accepted | 30 Sep 2020 |
Deposited | 28 Feb 2024 |
Output status | Published |
Digital Object Identifier (DOI) | https://doi.org/10.1159/000511934 |
Web of Science identifier | WOS:000598157000006 |
Language | English |
https://repository.mdx.ac.uk/item/z6585
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