Towards AI-driven minimally invasive needle interventions

Abstract

Abstract The overall complication rate during laparoscopic access is estimated to be as high as 14 %. Surgeons have to rely heavily on their experience and haptic perception while inserting the Veress needle or a trocar into the peritoneal cavity. Surgical Audio Guidance (SURAG) is a promising alternative to current techniques. It acquires instrument-born vibroacoustic (VA) waves to track the insertion of the instrument and provide real-time feedback to surgeons. This article presents an initial evaluation of the SURAG technology through two sets of experiments to classify Veress needle events using different AI-models. The results demonstrate the feasibility of using AI for classifying Veress needle events and the potential of the SURAG technology to support surgeons during laparoscopic access and minimally invasive needle interventions in general.