In this section
We use perceptual methods, AI, and frugal robotics innovation to deliver transformative diagnostic and treatment solutions.
The HARMS lab leverages perceptually enabled methodologies, artificial intelligence, and frugal innovation in robotics (such as soft surgical robots) to deliver transformative solutions for diagnosis and treatment. Our research is driven by both problem-solving and curiosity, aiming to build a comprehensive understanding of the actions, interactions, and reactions occurring in the operating room. We focus on using robotic technologies to facilitate procedures that are not yet widely adopted, particularly in endoluminal surgery, such as advanced treatments for gastrointestinal cancer.
......
....
Dr George Mylonas BEng, MSc, DIC, PhD
Senior Lecturer in Robotics and Technology in Cancer
Dr Adrian Rubio Solis
Research Associate in Sensing and Machine Learning
@article{Alian:2024:10.3389/frobt.2024.1372936,
author = {Alian, A and Avery, J and Mylonas, G},
doi = {10.3389/frobt.2024.1372936},
journal = {Front Robot AI},
title = {Tissue palpation in endoscopy using EIT and soft actuators.},
url = {http://dx.doi.org/10.3389/frobt.2024.1372936},
volume = {11},
year = {2024}
}
TY - JOUR
AB - The integration of soft robots in medical procedures has significantly improved diagnostic and therapeutic interventions, addressing safety concerns and enhancing surgeon dexterity. In conjunction with artificial intelligence, these soft robots hold the potential to expedite autonomous interventions, such as tissue palpation for cancer detection. While cameras are prevalent in surgical instruments, situations with obscured views necessitate palpation. This proof-of-concept study investigates the effectiveness of using a soft robot integrated with Electrical Impedance Tomography (EIT) capabilities for tissue palpation in simulated in vivo inspection of the large intestine. The approach involves classifying tissue samples of varying thickness into healthy and cancerous tissues using the shape changes induced on a hydraulically-driven soft continuum robot during palpation. Shape changes of the robot are mapped using EIT, providing arrays of impedance measurements. Following the fabrication of an in-plane bending soft manipulator, the preliminary tissue phantom design is detailed. The phantom, representing the descending colon wall, considers induced stiffness by surrounding tissues based on a mass-spring model. The shape changes of the manipulator, resulting from interactions with tissues of different stiffness, are measured, and EIT measurements are fed into a Long Short-Term Memory (LSTM) classifier. Train and test datasets are collected as temporal sequences of data from a single training phantom and two test phantoms, namely, A and B, possessing distinctive thickness patterns. The collected dataset from phantom B, which differs in stiffness distribution, remains unseen to the network, thus posing challenges to the classifier. The classifier and proposed method achieve an accuracy of 93 % and 88.1 % on phantom A and B, respectively. Classification results are presented through confusion matrices and heat maps, visualising the accuracy of the algorithm and correspond
AU - Alian,A
AU - Avery,J
AU - Mylonas,G
DO - 10.3389/frobt.2024.1372936
PY - 2024///
TI - Tissue palpation in endoscopy using EIT and soft actuators.
T2 - Front Robot AI
UR - http://dx.doi.org/10.3389/frobt.2024.1372936
UR - https://www.ncbi.nlm.nih.gov/pubmed/39184867
VL - 11
ER -
General enquiries
hamlyn@imperial.ac.uk
Facility enquiries
hamlyn.facility@imperial.ac.uk
The Hamlyn Centre
Bessemer Building
South Kensington Campus
Imperial College
London, SW7 2AZ
Map location
