Competences
Medical robotics operates in the crossroad between what is technologically possible in the field of industrial robotics and what is clinically needed. Hence we take advanced methods and algorithms from the industrial robotic domain and transfer it to the medical domain.
Modeling and control of robot motions for the automated surgical task.
- Planning and programming process trajectories
- Sensor-based control for compensation
- Programming by Demonstration
Medical Vision
- AI-based image analyses
- Vision-based tracking of surgical instruments
Facilities
We have a med medical robotic lab with the Raven II Surgical Platform – which is a robotic platform that is being used to develop and test new robotic actions. We have a dual UR arm surgical robot with tool mounts for fitting two generations of da Vinci surgical instruments. We have access to the animal lab at the Odense University Hospital.
Why it matters
In the coming years, Denmark will invest billions of Danish Kroners (DKK) in new hospitals and healthcare services. Locally in Odense “Nyt OUH” is estimated to cost 6.9 billion DKK. This will ensure 714 somatic beds in the new hospital reducing the number of beds by almost 35 % compared to 2012. At the same time, it is expected that the number of patients will increase in the coming years and the group of elderly requiring more treatment relatively. The global market for surgical robotics is expected to grow 17% in the coming years. With a reduced number of beds and an increased number of patients, the hospitals are forced to increase their efficiency.
Minimal invasive surgery using robots is one of these technologies that will reduce the hospital stay due to less intraoperative blood loss, intraoperative and postoperative transfused blood volume, and significantly lower surgical site infection rates thereby increase the capacity of the wards and operating theatre. Currently, the numbers of surgical procedures conducted using robots are still limited – however, more research on robotic surgery can lead to an impact in this area and lead to a further reduction in the hospital stay of the surgical patients. And by automating robotic surgery, surgical procedures can be executed without the surgeons who planned the surgery being physically present in the operational theatre.
Project examples
ROSOR is a collaboration between Odense University Hospital (OUH) and SDU on surgical robotic application research in gynecology. The main goal of this project is to bridge the development of automatic surgery through the Raven Surgical Platform.
AIID
Using AI-based image processing algorithms, AIID develops and implements cutting edge technology in medical image processing to detect disease levels in retina images of diabetic patients. We use convolutional neural network architecture to segment each pixel in fundus images to give human-readable results for the AI-based classification.
Combining robotic ultrasound scanning with AI-based image processing, the ROPCA Ultrasound project aims to create a robotic system for early detection and stable monitoring of rheumatoid arthritis (RA). Ultrasound (US) of the joints is shown to be a very sensitive method for detecting early stages of RA. However, US in diagnosis and disease management is prone to be operator dependent and is still subject to interobserver variability due to biases inherent to human expert evaluators. By streamlining the scanning of the joints using a robot, and grading the images using CNN based AI – this project will increase the quality of the diagnoses and monitoring of RA patients. This project was started on a private grant from Energi Fyn and later continued by a grant from the SDU Welfare Innovation initiative. It is a collaboration between Svendborg Sygehus and SDU Medical Robotics group.
For more information contact Thiusius Rajeeth Savarimuthu