This research explores the acquisition and analysis of vibroacoustic signals generated during tissue-tool interactions, using a conventional aspiration needle enhanced with a proximally mounted MEMS audio sensor, to extract temperature information. Minimally invasive temperature monitoring is critical in thermotherapy applications, but current methods often rely on additional sensors or simulations of typical tissue behavior. In this study, a commercially available needle was inserted into water-saturated foams with temperatures ranging from 25°C to 55°C, varied in 5-degree increments. Given that temperature affects the speed of sound, water's heat capacity, and the mechanical properties of most tissues, it was hypothesized that the vibroacoustic signals recorded during needle insertion would carry temperature-dependent information. The acquired signals were segmented, processed, and analyzed using signal processing techniques and a deep learning algorithm. Results demonstrated that the audio signals contained distinct temperature-dependent features, enabling temperature prediction with a root mean squared error of approximately 3°C. We present these initial laboratory findings, highlighting significant potential for refinement. This novel approach could pave the way for a real-time, minimally invasive method for thermal monitoring in medical applications.

This article combines a holistic look at needed developments toward autonomous robotic-assisted surgeries combined with a presentation on a novel sensor and analysis technology using vibroacoustic audio signals as a tool for guiding, sensing, and supporting technology.Healthcare will experience many changes in the next 10 years. Intentional technology-based disruptions will have an effect on actual delivery but also come with novel health business models. This article will initially present some of the upcoming developments and subsequently focus on the use of artificial intelligence (AI) and intelligent sensing for application in robotic assisted surgery (RAS).The combination of these technologies will lead to dedicated RASs in the near future with reduced cost and complexity and higher precision and prediction capabilities.While the procedures have significant advantages for the patient, they also have problems, as the navigation/guidance of the devices to a target location is based on either preoperatively acquired images and then performed freehand or accompanied by intraoperative imaging, such as MRI or CT, which is expensive and complicated and can produce artifacts or video-based technologies that only allow direct visualization. Using robotic systems for moving and guiding these interventional and therapeutic devices adds additional issues, such as a lack of palpation sensation and/or tissue feedback. While it is possible to add sensors to the distal tip, this creates other obstacles concerning reduced functionality, need for cables, sterility issues and added complexity and cost.We propose the use of a proximally attached audio sensor to record tissue tool interactions and provide real-time feedback to clinicians. This paper reports initial attempts to use this technology with robotic arms for surface characterization and interventional vascular procedures, which have recently gained increased attention in combination with robotic devices.Advanced sensors and AI will be powerful combinations not only for guiding and supporting robotic procedures but also for enabling surgical outcome prediction, which could theoretically increase the safety of RASs.