Sensor-equipped scalpels could revolutionise how surgeons are trained - this is according to researchers at Edinburgh University who studied data captured by the new scalpel during its trials
They discovered that the embedded sensors were highly effective in tracking the force applied by users during surgical procedures and overall device control.
This sensor data analysis proved to be just as accurate as traditional methods of evaluation, which typically rely on visual assessments by experienced practitioners.
The team believes that further development of this technology, featuring an advanced force-sensing system, could extend its applications to assess a wide range of surgical skills.
Notably, it could be crucial in developing robotic devices capable of performing surgical procedures with enhanced safety and efficiency.
This low-cost device, developed by researchers in the School of Informatics, consists of a scalpel connected to a sensor-laden circuit board integrated into the handle. The research team has also designed a machine-learning model to analyse the data collected while users apply force with the scalpel.
Until now, there has been a lack of tools to measure the critical aspect of force application during surgery in real-world settings.
According to the research team, this type of measurement has never been used in traditional assessments of surgical skills.
To test the new technology, the researchers tracked the performance of 12 medical students and two surgeons. At the same time, they conducted an elliptical incision, a procedure involving two curved cuts to the skin, often used to remove moles and skin lesions like melanoma.
The tests were conducted using synthetic materials that simulate the properties of human skin, made from gelatin and silicone.
The results were compared with the assessments made by four surgical experts, two neuroscientists, and two plastic surgeons. In general, the results closely matched the experts' reviews, suggesting that this technology could simplify the process of evaluating surgical skills. Some discrepancies did arise, attributed partly to differences in instrument and tissue handling techniques between neuroscientists and plastic surgeons.
Professor Ram Ramamoorthy from the School of Informatics said: ‘We are excited to develop this new system, which uses real-life sensing technology and machine learning methods to quantitatively assess surgical skill. This system will enable the development of new systems for skill assessment and training and could one day lead to the creation of automated surgical devices that can assist surgical teams.’
The study is published in Communications Engineering.
The research was supported by UK Research and Innovation (UKRI).
Photo credit: Rhona Crawford
