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Artificial intelligence and automation in endoscopy and surgery

Abstract

Modern endoscopy relies on digital technology, from high-resolution imaging sensors and displays to electronics connecting configurable illumination and actuation systems for robotic articulation. In addition to enabling more effective diagnostic and therapeutic interventions, the digitization of the procedural toolset enables video data capture of the internal human anatomy at unprecedented levels. Interventional video data encapsulate functional and structural information about a patient’s anatomy as well as events, activity and action logs about the surgical process. This detailed but difficult-to-interpret record from endoscopic procedures can be linked to preoperative and postoperative records or patient imaging information. Rapid advances in artificial intelligence, especially in supervised deep learning, can utilize data from endoscopic procedures to develop systems for assisting procedures leading to computer-assisted interventions that can enable better navigation during procedures, automation of image interpretation and robotically assisted tool manipulation. In this Perspective, we summarize state-of-the-art artificial intelligence for computer-assisted interventions in gastroenterology and surgery.

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Fig. 1: Artificial intelligence-based CAIs in gastrointestinal MIS.
Fig. 2: Artificial intelligence-based CADe and CADx systems.
Fig. 3: Artificial intelligence-based computer-assisted navigation.
Fig. 4: Example of artificial intelligence-based gesture recognition for robotic surgery suturing.
Fig. 5: Artifical intelligence-based robotically assisted surgery.

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Acknowledgements

The authors are supported by the Wellcome/EPSRC Centre for Interventional and Surgical Sciences (WEISS) at University College London (203145Z/16/Z), EPSRC (EP/P012841/1, EP/P027938/1 and EP/R004080/1) and the H2020 FET (GA 863146). D.S. is supported by a Royal Academy of Engineering Chair in Emerging Technologies (CiET1819\2\36) and an EPSRC Early Career Research Fellowship (EP/P012841/1).

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D.S. and F.C. researched data for the article and wrote the article. All authors contributed substantially to discussion of the content, and reviewed and/or edited the manuscript before submission.

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Correspondence to Danail Stoyanov.

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D.S. is part of Digital Surgery from Medtronic and a shareholder in Odin Vision. L.B.L. is a shareholder in Odin Vision. F.C. declares no competing interests.

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Nature Reviews Gastroenterology & Hepatology thanks Pietro Valdastri and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Chadebecq, F., Lovat, L.B. & Stoyanov, D. Artificial intelligence and automation in endoscopy and surgery. Nat Rev Gastroenterol Hepatol (2022). https://doi.org/10.1038/s41575-022-00701-y

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