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Cited by 125Artificial Intelligence in Healthcare and EducationSurgical Simulation and TrainingTechnology and Human Factors in Education and Health

CAI4CAI: The Rise of Contextual Artificial Intelligence in Computer Assisted Interventions

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Vercauteren, Tom, Unberath, Mathias, Padoy, Nicolas, Navab, Nassir·2020·Proceedings of the IEEE
DOI 10.1109/jproc.2019.2946993PMC PMC6952279
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Abstract

Data-driven computational approaches have evolved to enable extraction of information from medical images with a reliability, accuracy and speed which is already transforming their interpretation and exploitation in clinical practice. While similar benefits are longed for in the field of interventional imaging, this ambition is challenged by a much higher heterogeneity. Clinical workflows within interventional suites and operating theatres are extremely complex and typically rely on poorly integrated intra-operative devices, sensors, and support infrastructures. Taking stock of some of the most exciting developments in machine learning and artificial intelligence for computer assisted interventions, we highlight the crucial need to take context and human factors into account in order to address these challenges. Contextual artificial intelligence for computer assisted intervention, or CAI4CAI, arises as an emerging opportunity feeding into the broader field of surgical data science. Central challenges being addressed in CAI4CAI include how to integrate the ensemble of prior knowledge and instantaneous sensory information from experts, sensors and actuators; how to create and communicate a faithful and actionable shared representation of the surgery among a mixed human-AI actor team; how to design interventional systems and associated cognitive shared control schemes for online uncertainty-aware collaborative decision making ultimately producing more precise and reliable interventions.

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  • Machine learning methods, including deep neural networks, have enjoyed growing popularity across image analysis problems precisely because they do not require explicit definitions of complex functional mappings.

    “Machine learning methods, including deep neural networks (NNs), have enjoyed a growing popularity across a variety of image analysis problems (Vercauteren et al., 2019), precisely because they do not require explicit definitions of complex functional mappings.”

    From The Impact of Machine Learning on 2D/3D Registration for Image-Guided Interventions: A Systematic Review and Perspective (2021) · cites this paper as Vercauteren et al., 2019

    The Introduction describes machine learning across multiple computer-assisted intervention tasks, and the Intelligent Imaging in Interventional Biophotonics section notes its increasing use in hyperspectral imaging. The Contextual Learning for Image Registration section gives an example of data-driven methods that avoid explicitly modeling image-intensity correlations. That example does not establish that avoiding explicit definitions of complex functional mappings is the general reason for machine learning’s growing popularity across image analysis. (LLM, conf=0.84)

    Experimental — accuracy not yet measured

  • Because image-based navigation techniques are designed to seamlessly integrate into conventional surgical workflows, their use should-in theory-not cause frustration or prolonged procedure times.

    “Further, because image-based navigation techniques are designed to seamlessly integrate into conventional surgical workflows, their use should—in theory—not cause frustration or prolonged procedure times (Vercauteren et al., 2019).”

    From The Impact of Machine Learning on 2D/3D Registration for Image-Guided Interventions: A Systematic Review and Perspective (2021) · cites this paper as Vercauteren et al., 2019

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References 171 · Cited by 125

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Authors
Vercauteren, T., Unberath, M., Padoy, N., & Navab, N.
Affiliations
  • King's College London (GB)
  • Johns Hopkins University (US)
  • Centre National de la Recherche Scientifique (FR)
  • Technical University of Munich (DE)
Corresponding author
Vercauteren, Tom
ORCID
Venue
Proceedings of the IEEE
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Published
2020-01
Volume
108
Issue
1
Pages
198-214
ISSN
  • 0018-9219 (ISSN-L)
  • 1558-2256
DOI
10.1109/jproc.2019.2946993
PMC
PMC6952279
OpenAlex
W2980852587
Cited by
125
References
171
Citations by year
2026: 14 · 2025: 17 · 2024: 9 · 2023: 24 · 2022: 17 · 2021: 24 · 2020: 20
FWCI
4.85
Type
journal article
Language
en
Open access
Licence
Locations
  • Proceedings of the IEEE (publishedVersion, other-oa)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • Research Portal (King's College London) (publishedVersion, cc-by)
  • arXiv (Cornell University) (submittedVersion)
  • HAL (submittedVersion, cc-by)
  • Lirias (submittedVersion, other-oa)
  • arXiv (Cornell University) (cc-by)
Funders
  • Royal Academy of Engineering (RCSRF1819\7\34)
  • Wellcome Trust (203148/Z/16/Z, WT101957)
  • Engineering and Physical Sciences Research Council (NS/A000049/1, NS/A000027/1)
  • National Institute of Biomedical Imaging and Bioengineering (R01 EB0223939)
  • Johns Hopkins University
  • Fellowship of the Malone Center for Engineering in Healthcare, Johns Hopkins University
  • Agence Nationale de la Recherche (ANR-16-CE33-0009 (DeepSurg), ANR-18-CE45-0011-03 (OptimiX), ANR-11-LABX-0004 (Labex CAMI))
  • BPI France through Project CONDOR
Topics
  • Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
  • Health Sciences › Medicine › Surgery › Surgical Simulation and Training
  • Health Sciences › Medicine › Public Health, Environmental and Occupational Health › Technology and Human Factors in Education and Health
Keywords
computer-based intervention, interventional imaging, surgical data science, human-AI collaboration, shared control, decision making under uncertainty, clinical workflow integration

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