A machine learning optimized Dielectric Ultra-focused Oscillatory (DUO) electrode for low temperature electrosurgery

Abstract

The widespread adoption of radio frequency (RF) energy has made electrosurgery a cornerstone of modern surgical procedures, primarily due to its ability to minimize blood loss during, or independent of, tissue incision. Among the various electrosurgical modalities, monopolar electrodes have become indispensable in open surgeries and have been the focus of extensive research-exploring aspects such as electrode shape, material, surface coating, RF generator modulation, and feedback mechanisms involving temperature and impedance sensing. While electrosurgery delivers thermal energy for tissue cutting and coagulation, thermal effects represent both its principal utility and its greatest risk. Conventional monopolar electrodes operate at high temperatures (exceeding 250 degrees C) to achieve surgical efficacy, but such conditions often result in substantial thermal damage to surrounding tissue and implanted devices. In response to these challenges, we propose a novel blade-type monopolar electrode employing dielectric heating as the primary energy delivery method. Unlike conventional electrodes, which generate heat via ohmic loss at the electrode-tissue interface, the proposed Dielectric Ultra-Focused Oscillatory (DUO) blade directly heats tissue moisture through focused dielectric energy, effectively eliminating surface heating of the electrode. This mechanism naturally restricts the maximum temperature to 100 degrees C, governed by the phase transition of water vaporization. Experimental validation of the DUO blade demonstrated superior performance across key surgical metrics, including reduced operating temperature, shallower thermal necrosis depth, minimized blood loss, and decreased surgical smoke. These results underscore the DUO blade's potential to enhance surgical precision, safety, and visibility in electrosurgical applications.