Computer Vision in the Operating Room: Opportunities and Caveats
Abstract
Effectiveness of computer vision techniques has been demonstrated through a number of applications, both within and outside healthcare. The operating room environment specifically is a setting with rich data sources compatible with computational approaches and high potential for direct patient benefit. The aim of this review is to summarize major topics in computer vision for surgical domains. The major capabilities of computer vision are described as an aid to surgical teams to improve performance and contribute to enhanced patient safety. Literature was identified through leading experts in the fields of surgery, computational analysis and modeling in medicine, and computer vision in healthcare. The literature supports the application of computer vision principles to surgery. Potential applications within surgery include operating room vigilance, endoscopic vigilance, and individual and team-wide behavioral analysis. To advance the field, we recommend collecting and publishing carefully annotated datasets. Doing so will enable the surgery community to collectively define well-specified common objectives for automated systems, spur academic research, mobilize industry, and provide benchmarks with which we can track progress. Leveraging computer vision approaches through interdisciplinary collaboration and advanced approaches to data acquisition, modeling, interpretation, and integration promises a powerful impact on patient safety, public health, and financial costs.
Additional Information
© 2020 IEEE. Manuscript received September 25, 2020; accepted November 10, 2020. Date of publication November 24, 2020; date of current version February 22, 2021. This article was recommended for publication by Associate Editor P. Poignet and Editor P. Dario upon evaluation of the reviewers' comments. This work was supported by the National Heart, Lung, and Blood Institute of NIH (PI Zenati) under Grant R01HL126896. The work of Nicolas Padoy was supported in part by the French State Funds managed by the Agence Nationale de la Recherche through the Investissements d'Avenir Program under Grant ANR-16-CE33-0009 (DeepSurg), Grant ANR-11-LABX-0004 (Labex CAMI), Grant ANR-10-IDEX-0002-02 (Idex Unistra), and Grant ANR-10-IAHU-02 (IHU Strasbourg), and in part by BPI France through Project CONDOR.Attached Files
Accepted Version - nihms-1658718.pdf
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Additional details
- PMCID
- PMC7908934
- Eprint ID
- 108252
- DOI
- 10.1109/tmrb.2020.3040002
- Resolver ID
- CaltechAUTHORS:20210301-131304383
- R01HL126896
- NIH
- ANR-16-CE33-0009
- Agence Nationale pour la Recherche (ANR)
- ANR-11- LABX-0004
- Agence Nationale pour la Recherche (ANR)
- ANR-10-IDEX-0002-02
- Agence Nationale pour la Recherche (ANR)
- ANR-10-IAHU-02
- Agence Nationale pour la Recherche (ANR)
- BPI France
- Created
-
2021-03-01Created from EPrint's datestamp field
- Updated
-
2022-02-10Created from EPrint's last_modified field