Technological advancements in surgical laparoscopy considering artificial intelligence: a survey among surgeons in Germany

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Abstract

Purpose

The integration of artificial intelligence (AI) into surgical laparoscopy has shown promising results in recent years. This survey aims to investigate the inconveniences of current conventional laparoscopy and to evaluate the attitudes and desires of surgeons in Germany towards new AI-based laparoscopic systems.

Methods

A 12-item web-based questionnaire was distributed to 38 German university hospitals as well as to a Germany-wide voluntary hospital association (CLINOTEL) consisting of 66 hospitals between July and November 2022.

Results

A total of 202 questionnaires were completed. The majority of respondents (88.1%) stated that they needed one assistant during laparoscopy and rated the assistants’ skillfulness as “very important” (39.6%) or “important” (49.5%). The most uncomfortable aspects of conventional laparoscopy were inappropriate camera movement (73.8%) and lens condensation (73.3%). Selected features that should be included in a new laparoscopic system were simple and intuitive maneuverability (81.2%), automatic de-fogging (80.7%), and self-cleaning of camera (77.2%). Furthermore, AI-based features were improvement of camera positioning (71.3%), visualization of anatomical landmarks (67.3%), image stabilization (66.8%), and tissue damage protection (59.4%). The reason for purchasing an AI-based system was to improve patient safety (86.1%); the reasonable price was €50.000–100.000 (34.2%), and it was expected to replace the existing assistants’ workflow up to 25% (41.6%).

Conclusion

Simple and intuitive maneuverability with improved and image-stabilized camera guidance in combination with a lens cleaning system as well as AI-based augmentation of anatomical landmarks and tissue damage protection seem to be significant requirements for the further development of laparoscopic systems.

Supplementary Information

The online version contains supplementary material available at 10.1007/s00423-023-03134-6.

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Most cited references 34

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Computer Vision Analysis of Intraoperative Video: Automated Recognition of Operative Steps in Laparoscopic Sleeve Gastrectomy

Daniel Hashimoto, Guy Rosman, Elan Witkowski … (2019)

To develop and assess AI algorithms to identify operative steps in laparoscopic sleeve gastrectomy (LSG). Computer vision, a form of artificial intelligence (AI), allows for quantitative analysis of video by computers for identification of objects and patterns, such as in autonomous driving. Intraoperative video from LSG from an academic institution were annotated by two fellowship-trained, board-certified bariatric surgeons. Videos were segmented into the following steps: 1) port placement, 2) liver retraction, 3) liver biopsy, 4) gastrocolic ligament dissection, 5) stapling of the stomach, 6) bagging specimen, and 7) final inspection of staple line. Deep neural networks were used to analyze videos. Accuracy of operative step identification by the AI was determined by comparing to surgeon annotations. 88 cases of LSG were analyzed. A random 70% sample of these clips were used to train the AI and 30% to test the AI’s performance. Mean concordance correlation coefficient for human annotators was 0.862, suggesting excellent agreement. Mean (±SD) accuracy of the AI in identifying operative steps in the test set was 82% ± 4% with a maximum of 85.6%. AI can extract quantitative surgical data from video with 85.6% accuracy. This suggests operative video could be used as a quantitative data source for research in intraoperative clinical decision support, risk prediction, or outcomes studies. The goal of this study was to develop and assess AI algorithms to identify operative steps in laparoscopic sleeve gastrectomy (LSG). 88 videos were analyzed. Mean accuracy of the algorithms was 82% ± 4% in identifying steps of LSG.

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The Virtual Operative Assistant: An explainable artificial intelligence tool for simulation-based training in surgery and medicine

Nykan Mirchi, Vincent Bissonnette, Recai Yilmaz … (2020)

Simulation-based training is increasingly being used for assessment and training of psychomotor skills involved in medicine. The application of artificial intelligence and machine learning technologies has provided new methodologies to utilize large amounts of data for educational purposes. A significant criticism of the use of artificial intelligence in education has been a lack of transparency in the algorithms’ decision-making processes. This study aims to 1) introduce a new framework using explainable artificial intelligence for simulation-based training in surgery, and 2) validate the framework by creating the Virtual Operative Assistant, an automated educational feedback platform. Twenty-eight skilled participants (14 staff neurosurgeons, 4 fellows, 10 PGY 4–6 residents) and 22 novice participants (10 PGY 1–3 residents, 12 medical students) took part in this study. Participants performed a virtual reality subpial brain tumor resection task on the NeuroVR simulator using a simulated ultrasonic aspirator and bipolar. Metrics of performance were developed, and leave-one-out cross validation was employed to train and validate a support vector machine in Matlab. The classifier was combined with a unique educational system to build the Virtual Operative Assistant which provides users with automated feedback on their metric performance with regards to expert proficiency performance benchmarks. The Virtual Operative Assistant successfully classified skilled and novice participants using 4 metrics with an accuracy, specificity and sensitivity of 92, 82 and 100%, respectively. A 2-step feedback system was developed to provide participants with an immediate visual representation of their standing related to expert proficiency performance benchmarks. The educational system outlined establishes a basis for the potential role of integrating artificial intelligence and virtual reality simulation into surgical educational teaching. The potential of linking expertise classification, objective feedback based on proficiency benchmarks, and instructor input creates a novel educational tool by integrating these three components into a formative educational paradigm.

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Artificial Intelligence for Surgical Safety: Automatic Assessment of the Critical View of Safety in Laparoscopic Cholecystectomy Using Deep Learning

Pietro Mascagni, Armine Vardazaryan, Deepak Alapatt … (2020)

To develop a deep learning model to automatically segment hepatocystic anatomy and assess the criteria defining the critical view of safety (CVS) in laparoscopic cholecystectomy (LC).

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Author and article information

Contributors

Sebastian Lünse: sebastian.luense@uk-brandenburg.de

Eric L. Wisotzky: eric.wisotzky@hhi.fraunhofer.de

Sophie Beckmann: sophie.beckmann@hhi.fraunhofer.de

Christoph Paasch: christoph.paasch@uk-brandenburg.de

Richard Hunger: richard.hunger@mhb-fontane.de

René Mantke: mantke.mhb@uk-brandenburg.de

Journal

Journal ID (nlm-ta): Langenbecks Arch Surg

Journal ID (iso-abbrev): Langenbecks Arch Surg

Title: Langenbeck's Archives of Surgery

Publisher: Springer Berlin Heidelberg (Berlin/Heidelberg )

ISSN (Print): 1435-2443

ISSN (Electronic): 1435-2451

Publication date (Electronic): 16 October 2023

Publication date PMC-release: 16 October 2023

Publication date (Print): 2023

Volume: 408

Issue: 1

Electronic Location Identifier: 405

Affiliations

[1 ]GRID grid.473452.3, Department of General and Visceral Surgery, Brandenburg Medical School, , University Hospital Brandenburg/Havel, ; Hochstrasse 29, 14770 Brandenburg, Germany

[2 ]GRID grid.435231.2, ISNI 0000 0004 0495 5488, Vision and Imaging Technologies, , Fraunhofer Heinrich-Hertz-Institut HHI, ; Einsteinufer 37, 10587 Berlin, Germany

[3 ]Department of Computer Science, Humboldt-Universität Zu Berlin, ( https://ror.org/01hcx6992) Unter Den Linden 6, 10117 Berlin, Germany

[4 ]GRID grid.473452.3, Faculty of Health Science Brandenburg, Brandenburg Medical School, , University Hospital Brandenburg/Havel, ; 14770 Brandenburg, Germany

Article

Publisher ID: 3134

DOI: 10.1007/s00423-023-03134-6

PMC ID: 10579134

PubMed ID: 37843584

SO-VID: 72799214-0c13-496e-af8b-9faac7c78868

License:

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.

History

Date received : 13 June 2023

Date accepted : 2 October 2023

Funding

Funded by: Medizinische Hochschule Brandenburg CAMPUS gGmbH (5634)

Open Access :

Open Access funding enabled and organized by Projekt DEAL.

Custom metadata

ScienceOpen disciplines: Surgery

Keywords: laparoscopic surgery,artificial intelligence,survey

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ScienceOpen disciplines: Surgery

Keywords: laparoscopic surgery, artificial intelligence, survey

Technological advancements in surgical laparoscopy considering artificial intelligence: a survey among surgeons in Germany

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Abstract

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Supplementary Information

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Artificial Intelligence in Medicine

Most cited references 34

Computer Vision Analysis of Intraoperative Video: Automated Recognition of Operative Steps in Laparoscopic Sleeve Gastrectomy

The Virtual Operative Assistant: An explainable artificial intelligence tool for simulation-based training in surgery and medicine

Artificial Intelligence for Surgical Safety: Automatic Assessment of the Critical View of Safety in Laparoscopic Cholecystectomy Using Deep Learning

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