Skip to main navigation Skip to search Skip to main content

Intestinal anastomosis automation through diffuse reflectance spectroscopy: Towards real time guidance during robotic surgery

  • University College Cork
  • Lee Maltings Complex

Research output: Chapter in Book/Report/Conference proceedingsConference proceedingpeer-review

Abstract

Intestinal anastomosis is a surgical procedure to reconnect two portions of the intestine and restore bowel continuity after removal of pathological elements. Anastomosis is required in nearly all the >330,000 bowel resections performed annually in the USA and England. Anastomotic leakage (AL) has been reported to happen in 2.6%-19% of cases depending on multiple factors such as the definition, location and type of anastomosis, as well as the cohort under investigation. Mortality rates due to AL vary between 10%-20%. AL occurs primarily when tissues are not healed after suture. Healing is affected by the restoration of local blood flow as well as matching tissue layers during suture. Ensuring this healing requires millimetric accuracy and consistency from surgeons despite the movement due to patient breathing and other factors, making anastomosis the most challenging step in gastrointestinal surgery. Current robotic surgery has advanced to enable laparoscopic surgery without human help. However, the potential of robotic surgery to lower AL rates is still hindered by the missing tissue identification due to the lack of tactile feedback by surgeons, which does not allow matching tissue layers during suture. Tissue identification can be performed by using diffuse reflectance spectroscopy with the aim of excluding fat and including only mucosa and muscle tissues in sutures. In this study, we have classify fat, mucosa and muscle tissues based on 3125 DRS measurements of freshly excised ex vivo specimens of 47 patients. DRS measurements were performed with fiber-optic probes of 630-μm source detector distance (SDD; probe 1) and 2500-μm SDD (probe 2) to measure tissue layers from ∼0.5-1mm and from ∼0.5-2 mm deep, respectively. By using probe 1 and 5-fold cross-validation of quadratic support vector machine (SVM) models, we obtained true positive rates of (97.9 ±1.8) % for fat tissues, (95.5 ±1.3) % for mucosa, and (95.5 ±1.1) % for muscle. Similarly for probe 2, we achieved true positive rates of (96.7 ±1.3) % for fat tissues, (95.7 ±1.0) % for mucosa, and (94.5 ±1.9) % for muscle.

Original languageEnglish
Title of host publicationHigh-Speed Biomedical Imaging and Spectroscopy IX
EditorsKevin K. Tsia, Keisuke Goda
PublisherSPIE
ISBN (Electronic)9781510669659
DOIs
Publication statusPublished - 2024
EventHigh-Speed Biomedical Imaging and Spectroscopy IX 2024 - San Francisco, United States
Duration: 27 Jan 202428 Jan 2024

Publication series

NameProgress in Biomedical Optics and Imaging - Proceedings of SPIE
Volume12853
ISSN (Print)1605-7422

Conference

ConferenceHigh-Speed Biomedical Imaging and Spectroscopy IX 2024
Country/TerritoryUnited States
CitySan Francisco
Period27/01/2428/01/24

Keywords

  • Artificial intelligence
  • Biomedical optics
  • Biophotonics.
  • Diffuse reflectance spectroscopy
  • Elastic scattering spectroscopy
  • Intestinal anastomosis
  • Machine learning
  • Multivariate analysis
  • Near-infrared spectroscopy
  • Optical diagnostics
  • Optical spectroscopy
  • Robotic surgery
  • Surgical guidance
  • Surgical robots
  • Suture

Fingerprint

Dive into the research topics of 'Intestinal anastomosis automation through diffuse reflectance spectroscopy: Towards real time guidance during robotic surgery'. Together they form a unique fingerprint.

Cite this