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Towards human action recognition during surgeries using de-identified video data

  • With the progress of technology in modern hospitals, an intelligent perioperative situation recognition will gain more relevance due to its potential to substantially improve surgical workflows by providing situation knowledge in real-time. Such knowledge can be extracted from image data by machine learning techniques but poses a privacy threat to the staff’s and patients’ personal data. De-identification is a possible solution for removing visual sensitive information. In this work, we developed a YOLO v3 based prototype to detect sensitive areas in the image in real-time. These are then deidentified using common image obfuscation techniques. Our approach shows that it is principle suitable for de-identifying sensitive data in OR images and contributes to a privacyrespectful way of processing in the context of situation recognition in the OR.

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Metadaten
Author of HS ReutlingenBach, Thanh Nam; Junger, Denise; Curio, Cristóbal; Burgert, Oliver
URN:urn:nbn:de:bsz:rt2-opus4-37513
DOI:https://doi.org/10.1515/cdbme-2022-0028
Erschienen in:Current directions in biomedical engineering
Publisher:De Gruyter
Place of publication:Berlin
Document Type:Article
Language:English
Year of Publication:2022
Tag:YOLO; action recognition; de-identification; image data; sensitive information
Volume:8
Issue:1
Page Number:4
First Page:109
Last Page:112
DDC classes:570 Biowissenschaften, Biologie
Open Access?:Ja
Licence (German):License Logo  Creative Commons - CC BY - Namensnennung 4.0 International