ReviewInnovative surgical sciences2017
Surgical data processing for smart intraoperative assistance systems.
Review in Innovative surgical sciences, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
5 citing papers in PubMed.
- Fundamentals of Arthroscopic Surgery Training and beyond: a reinforcement learning exploration and benchmark.International journal of computer assisted radiology and surgery · 2024Article
- State-of-the-art of situation recognition systems for intraoperative procedures.Medical & biological engineering & computing · 2022Review
- Attitudes of the Surgical Team Toward Artificial Intelligence in Neurosurgery: International 2-Stage Cross-Sectional Survey.World neurosurgery · 2021Article
- Review
- Surgery 4.0: the natural culmination of the industrial revolution?Innovative surgical sciences · 2017Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Different components of the newly defined field of surgical data science have been under research at our groups for more than a decade now. In this paper, we describe our sensor-driven approaches to workflow recognition without the need for explicit models, and our current aim is to apply this knowledge to enable context-aware surgical assistance systems, such as a unified surgical display and robotic assistance systems. The methods we evaluated over time include dynamic time warping, hidden Markov models, random forests, and recently deep neural networks, specifically convolutional neural networks.
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Registered trials
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