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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.
Abstract
The proposed system aims at reproducibility in ultrasound follow-up examinations by storing the position of the ultrasound transducer in relation to acquired images. The system utilizes an electromagnetic tracking system. It includes a guidance feature to assist in placing the transducer correctly on the patient. Evaluation of the system involved technical accuracy tests on a phantom and accuracy tests on human subjects. The results showed that the technical accuracy of the system met the required criteria, but the transducer positioning error in realistic scenarios was above the threshold. Usability testing indicated potential benefits for medical training and provided suggestions for improving the user interface. Overall, the system shows promise for further development and can already be used for training ultrasound examinations.
