A generative force model for surgical skill quantification using sensorised instruments

Artūras Straižys, Michael Burke, Paul M. Brennan, Subramanian Ramamoorthy

Research output: Contribution to journalArticleResearchpeer-review


Surgical skill requires the manipulation of soft viscoelastic media. Its measurement through generative models is essential both for accurate quantification of surgical ability and for eventual automation in robotic platforms. Here we describe a sensorised scalpel, along with a generative model to assess surgical skill in elliptical excision, a representative manipulation task. Our approach allows us to capture temporal features via data collection and downstream analysis. We demonstrate that incision forces carry information that is relevant for skill interpretation, but inaccessible via conventional descriptive statistics. We tested our approach on 12 medical students and two practicing surgeons using a tissue phantom mimicking the properties of human skin. We demonstrate that our approach can bring deeper insight into performance analysis than traditional time and motion studies, and help to explain subjective assessor skill ratings. Our technique could be useful in applications spanning forensics, pathology as well as surgical skill quantification.
Original languageEnglish
Article number36
Number of pages11
JournalCommunications Engineering
Publication statusPublished - 10 Jun 2023

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