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Machine learning-based detection of electrosurgical device-induced bleeding in laparoscopic videos of diaphragmatic hernia repair

Published Online: 2025-08-18

Published Print: 2025-09

Authors

Ribbens, Vincent J.

Baltus, Simon C. https://orcid.org/0009-0004-5074-7548
Tan, Can Ozan

Broeders, Ivo A. M. J.
Funding

Funding for this research was provided by:

Johnson and Johnson
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Text and Data Mining valid from 2025-08-18

Version of Record valid from 2025-08-18
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Article History

Received: 3 June 2025

Accepted: 2 August 2025

First Online: 18 August 2025

Declarations

:

: Research performed by Simon Baltus and Ivo Broeders is sponsored by Johnson & Johnson MedTech. Ivo Broeders is a proctor for Intuitive Surgical. Vincent Ribbens and Can Ozan Tan have no conflict of interest to report.

: This research study was conducted prospectively using anonymized video data obtained during routine surgical care. The study protocol was reviewed by the Medical research Ethics Committees United (MEC-U), which determined that ethical approval was not required (registration number W23.075).

: Not applicable. The use of anonymized and non-identifiable data obtained during routine surgical care did not require informed consent, following the decision of the MEC-U.

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