Machine Learning Detection of Peritoneal Entry from Vibroacoustic Signals During Veress Needle Insertion

Background/Objectives: Establishing access is a hazardous step of laparoscopic surgery, and the widely used Veress needle offers only subjective tactile and auditory cues for peritoneal entry. Proximal vibroacoustic (VA) sensing captures needle–tissue interaction non-invasively and is known to contain a characteristic peritoneal puncture signature, which could help surgeons to precisely identify peritoneal entry. However, the VA signature has been evaluated only by retrospective, video-anchored and manual signal analysis. We investigate whether peritoneal entry can instead be detected automatically.