Literature DB >> 33447601

Computer Vision in the Surgical Operating Room.

François Chadebecq1, Francisco Vasconcelos1, Evangelos Mazomenos1, Danail Stoyanov1.   

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.
Copyright © 2020 by S. Karger AG, Basel.

Entities:  

Keywords:  Artificial intelligence; Computer vision; Computer-assisted intervention; Minimally invasive surgery

Year:  2020        PMID: 33447601      PMCID: PMC7768144          DOI: 10.1159/000511934

Source DB:  PubMed          Journal:  Visc Med        ISSN: 2297-4725


  33 in total

Review 1.  Surgical vision.

Authors:  Danail Stoyanov
Journal:  Ann Biomed Eng       Date:  2011-10-20       Impact factor: 3.934

2.  Analysis of eye gaze: do novice surgeons look at the same location as expert surgeons during a laparoscopic operation?

Authors:  Rana S A Khan; Geoffrey Tien; M Stella Atkins; Bin Zheng; Ormond N M Panton; Adam T Meneghetti
Journal:  Surg Endosc       Date:  2012-06-26       Impact factor: 4.584

3.  Face detection in the operating room: comparison of state-of-the-art methods and a self-supervised approach.

Authors:  Thibaut Issenhuth; Vinkle Srivastav; Afshin Gangi; Nicolas Padoy
Journal:  Int J Comput Assist Radiol Surg       Date:  2019-04-09       Impact factor: 2.924

4.  Optical techniques for 3D surface reconstruction in computer-assisted laparoscopic surgery.

Authors:  L Maier-Hein; P Mountney; A Bartoli; H Elhawary; D Elson; A Groch; A Kolb; M Rodrigues; J Sorger; S Speidel; D Stoyanov
Journal:  Med Image Anal       Date:  2013-05-03       Impact factor: 8.545

5.  Transanal Minimally Invasive Surgery (TAMIS): Standardizing a Reproducible Procedure.

Authors:  Sujata Gill; Jamil L Stetler; Ankit Patel; Virginia O Shaffer; Jahnavi Srinivasan; Charles Staley; S Scott Davis; Edward Lin; Patrick S Sullivan
Journal:  J Gastrointest Surg       Date:  2015-05-28       Impact factor: 3.452

6.  Accurate and interpretable evaluation of surgical skills from kinematic data using fully convolutional neural networks.

Authors:  Hassan Ismail Fawaz; Germain Forestier; Jonathan Weber; Lhassane Idoumghar; Pierre-Alain Muller
Journal:  Int J Comput Assist Radiol Surg       Date:  2019-07-30       Impact factor: 2.924

Review 7.  Vision-based and marker-less surgical tool detection and tracking: a review of the literature.

Authors:  David Bouget; Max Allan; Danail Stoyanov; Pierre Jannin
Journal:  Med Image Anal       Date:  2016-09-13       Impact factor: 8.545

8.  Standardization of operative technique in minimally invasive right hepatectomy: improving cost-value relationship through value stream mapping in hepatobiliary surgery.

Authors:  Mohammad R Jajja; Daniel Maxwell; Salila S Hashmi; Rebecca S Meltzer; Edward Lin; John F Sweeney; Juan M Sarmiento
Journal:  HPB (Oxford)       Date:  2018-10-22       Impact factor: 3.647

Review 9.  Deep learning with convolutional neural network for objective skill evaluation in robot-assisted surgery.

Authors:  Ziheng Wang; Ann Majewicz Fey
Journal:  Int J Comput Assist Radiol Surg       Date:  2018-09-25       Impact factor: 2.924

Review 10.  Augmented and Mixed Reality: Technologies for Enhancing the Future of IR.

Authors:  Brian J Park; Stephen J Hunt; Charles Martin; Gregory J Nadolski; Bradford J Wood; Terence P Gade
Journal:  J Vasc Interv Radiol       Date:  2020-02-13       Impact factor: 3.464

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  2 in total

Review 1.  Surgical data science - from concepts toward clinical translation.

Authors:  Lena Maier-Hein; Matthias Eisenmann; Duygu Sarikaya; Keno März; Toby Collins; Anand Malpani; Johannes Fallert; Hubertus Feussner; Stamatia Giannarou; Pietro Mascagni; Hirenkumar Nakawala; Adrian Park; Carla Pugh; Danail Stoyanov; Swaroop S Vedula; Kevin Cleary; Gabor Fichtinger; Germain Forestier; Bernard Gibaud; Teodor Grantcharov; Makoto Hashizume; Doreen Heckmann-Nötzel; Hannes G Kenngott; Ron Kikinis; Lars Mündermann; Nassir Navab; Sinan Onogur; Tobias Roß; Raphael Sznitman; Russell H Taylor; Minu D Tizabi; Martin Wagner; Gregory D Hager; Thomas Neumuth; Nicolas Padoy; Justin Collins; Ines Gockel; Jan Goedeke; Daniel A Hashimoto; Luc Joyeux; Kyle Lam; Daniel R Leff; Amin Madani; Hani J Marcus; Ozanan Meireles; Alexander Seitel; Dogu Teber; Frank Ückert; Beat P Müller-Stich; Pierre Jannin; Stefanie Speidel
Journal:  Med Image Anal       Date:  2021-11-18       Impact factor: 13.828

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

Authors:  Guillaume Kugener; Dhiraj J Pangal; Tyler Cardinal; Casey Collet; Elizabeth Lechtholz-Zey; Sasha Lasky; Shivani Sundaram; Nicholas Markarian; Yichao Zhu; Arman Roshannai; Aditya Sinha; X Y Han; Vardan Papyan; Andrew Hung; Animashree Anandkumar; Bozena Wrobel; Gabriel Zada; Daniel A Donoho
Journal:  JAMA Netw Open       Date:  2022-03-01
  2 in total

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