2022
DOI: 10.1038/s41746-022-00707-5
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Computer vision in surgery: from potential to clinical value

Abstract: Hundreds of millions of operations are performed worldwide each year, and the rising uptake in minimally invasive surgery has enabled fiber optic cameras and robots to become both important tools to conduct surgery and sensors from which to capture information about surgery. Computer vision (CV), the application of algorithms to analyze and interpret visual data, has become a critical technology through which to study the intraoperative phase of care with the goals of augmenting surgeons’ decision-making proce… Show more

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Cited by 47 publications
(25 citation statements)
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“…For instance, algorithms for identification of seminal vesicles and the small intestine were trained on comparably small datasets of 336 and 104 annotated images, respectively (Appendix D), while over 1000 annotated images were used to train models in the aforementioned work. Integration of more and diverse – ideally multicentric – training data would likely improve segmentation performance and increase clinical relevance of respective models [34]. One of the main reasons for scarcity of annotated laparoscopic datasets is the high annotation effort required for an integration of clinically meaningful knowledge into a dataset [35].…”
Section: Discussionmentioning
confidence: 99%
“…For instance, algorithms for identification of seminal vesicles and the small intestine were trained on comparably small datasets of 336 and 104 annotated images, respectively (Appendix D), while over 1000 annotated images were used to train models in the aforementioned work. Integration of more and diverse – ideally multicentric – training data would likely improve segmentation performance and increase clinical relevance of respective models [34]. One of the main reasons for scarcity of annotated laparoscopic datasets is the high annotation effort required for an integration of clinically meaningful knowledge into a dataset [35].…”
Section: Discussionmentioning
confidence: 99%
“…6 Additionally, intelligent image processing software providing autonomous segmentation and classification, increasingly implemented in mixed reality environments, could show to become highly beneficial for the development of image-guided visual navigation in a broad spectrum of surgical fields. 7…”
Section: Discussion and Outlookmentioning
confidence: 99%
“…7 Medical AI tools have since evolved to incorporate more complex methods, such as deep learning, that have yielded automated preliminary reads in diagnostic radiology and pathology as well as advanced surgical guidance systems. 8,9…”
Section: A Brief History Of Aimentioning
confidence: 99%