Smart scalpels could improve surgeon training and lead to more procedures by robots

Researchers who studied data captured by the scalpel during trials found its sensors could accurately track how much force users were applying during surgical procedures, and how they were controlling the device over time. Their findings are published in Communications Engineering.

Sensor data analysis showed the technology – which is equipped with a newly developed force-sensing system – could assess this skill as effectively as traditional evaluation methods, which involve visual assessment by experienced practitioners.

According to the University, the low-cost device consists of a scalpel connected to a sensor-loaded circuit board fitted inside its handle. The research team designed a machine learning model that analyses data captured as force is applied by scalpel users.

While the level of force applied is known to be important in surgery, there have been few tools until now capable of measuring it in real-life settings. These types of measurements have also never been used in traditional assessments of surgical skill, the team said.

Researchers tested the new technology by tracking 12 medical students and two surgeons as they carried out an elliptical incision, a procedure that involves making two curved cuts to the skin and is used to remove moles and skin legions. Tests were performed on synthetic material made of gelatin and silicone that mimics the properties of human skin.

Data analysis of each participant’s skills was compared with naked eye assessments made by two neuroscientists and two plastic surgeons.

Results broadly matched surgical experts’ assessment of each user’s ability, suggesting this type of technology could help simplify the process of assessing surgical skills.

Some discrepancies arose, partly because neuroscientists and plastic surgeons have differing instrument and tissue handling techniques, the team said.

In a statement, Professor Ram Ramamoorthy, of Edinburgh University’s School of Informatics, said: “We are excited to develop this new system, which uses a combination of 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 research was supported by UK Research and Innovation (UKRI).