The proposed system is capable to display intraoperative scattered radiation intuitively in 3D by using augmented reality. This can have a strong impact on improving clinicians' awareness of their exposure to ionizing radiation and on reducing overexposure risks.

No abstract

Perceiving and making sense of the surgical scene during Total Knee Arthroplasty (TKA) surgery is crucial for building assistance and decision support systems for surgeons and their team. However, the need for large volumes of annotated and structured data for AI-based methods hinders the development of such tools. We hereby present a study on the use of transfer learning to train deep neural networks with scarce annotated data to automatically detect bony areas on live images. We provide quantitative evaluation results on in-vivo data, captured during several TKA procedures. We hope that this work will facilitate further developments of smart surgical assistance tools for orthopaedic surgery.

The system enables the user to see the 3-D propagation of radiation, the medical staff's exposure, and/or the doses deposited on the patient's surface as seen through his own eyes.