Suture Breakage Warning System for Robotic Surgery.

Journal Article (Journal Article)

As robotic surgery has increased in popularity, the lack of haptic feedback has become a growing issue due to the application of excessive forces that may lead to clinical problems such as intraoperative and postoperative suture breakage. Previous suture breakage warning systems have largely depended on visual and/or auditory feedback modalities, which have been shown to increase cognitive load and reduce operator performance. This work catalogues a new sensing technology and haptic feedback system (HFS) that can reduce instances of suture failure without negatively impacting performance outcomes including knot quality. Suture breakage is common in knot-tying as the pulling motion introduces prominent shear forces. A shear sensor mountable on the da Vinci robotic surgical system's Cadiere grasper detects forces that correlate to the suture's internal tension. HFS then provides vibration feedback to the operator as forces near a particular material's failure load. To validate the system, subjects tightened a total of four knots, two with the Haptic Feedback System (HFS) and two without feedback. The number of suture breakages were recorded and knot fidelity was evaluated by measuring knot slippage. Results showed that instances of suture failure were significantly reduced when HFS was enabled (p = 0.0078). Notably, knots tied with HFS also showed improved quality compared to those tied without feedback (p = 0.010). The results highlight the value of HFS in improving robotic procedure outcomes by reducing instances of suture failures, producing better knots, and reducing the need for corrective measures.

Full Text

Duke Authors

Cited Authors

  • Abiri, A; Askari, SJ; Tao, A; Juo, Y-Y; Dai, Y; Pensa, J; Candler, R; Dutson, EP; Grundfest, WS

Published Date

  • April 2019

Published In

Volume / Issue

  • 66 / 4

Start / End Page

  • 1165 - 1171

PubMed ID

  • 30207946

Pubmed Central ID

  • PMC6494981

Electronic International Standard Serial Number (EISSN)

  • 1558-2531

Digital Object Identifier (DOI)

  • 10.1109/TBME.2018.2869417

Language

  • eng

Conference Location

  • United States