Sensor-Based Automated Detection of Electrosurgical Cautery States

Abstract

In computer-assisted surgery, it is typically required to detect when the tool comes into contact with the patient. In activated electrosurgery, this is known as the energy event. By continuously tracking the electrosurgical tools’ location using a navigation system, energy events can help determine locations of sensor-classified tissues. Our objective was to detect the energy event and determine the settings of electrosurgical cautery—robustly and automatically based on sensor data. This study aims to demonstrate the feasibility of using the cautery state to detect surgical incisions, without disrupting the surgical workflow. We detected current changes in the wires of the cautery device and grounding pad using non-invasive current sensors and an oscilloscope. An open-source software was implemented to apply machine learning on sensor data to detect energy events and cautery settings. Our methods classified each cautery state at an average accuracy of 95.56% across different tissue types and energy level parameters altered by surgeons during an operation. Our results demonstrate the feasibility of automatically identifying energy events during surgical incisions, which could be an important safety feature in robotic and computer-integrated surgery. This study provides a key step towards locating tissue classifications during breast cancer operations and reducing the rate of positive margins.

Sensor-Based Measurement Method to Support the Assessment of Robot-Assisted Radiofrequency Ablation

“…Robotic control and technology assistance, including medical 3D printing, can improve the accuracy of the procedures, as shown in previous studies [ 4 , 5 ]. Technology-driven methods, including robotics and Artificial Intelligence (AI), are gaining more importance in the medical field, supporting various aspects of digital surgery [ 6 , 7 ].…”