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Human-Centred Automation, Robotics and Monitoring

We use perceptual methods, AI, and frugal robotics innovation to deliver transformative diagnostic and treatment solutions.

Head of Group

Dr George Mylonas

B415B Bessemer Building
South Kensington Campus

+44 (0)20 3312 5145

YouTube ⇒ HARMS Lab

What we do

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.

Meet the team

Mr Amirhosein Alian
Research Postgraduate

Dr James P Avery
Honorary Lecturer

Mr Junhong Chen
Research Postgraduate

Mr Kaizhong Deng
Research Postgraduate

Mr Hanqi LOU
Research Postgraduate

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

Dr Jianlin Yang
Research Associate

Mr Zhangxi Zhou
Research Postgraduate

Citation

BibTex format

@inproceedings{Avila:2016,
author = {Avila, Rencoret FB and Elson, D and Mylonas, G},
title = {A Robotic Hyperspectral Scanning Framework for Endoscopy},
url = {http://hdl.handle.net/10044/1/40268},
year = {2016}
}

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RIS format (EndNote, RefMan)

TY - CPAPER
AB - Gastrointestinal (GI) endoscopy is the gold-standard procedure for detection and treatment of dysplastic lesions and early stage GI cancers. Despite its proven effectiveness, its sensitivity remains suboptimal due to the subjective nature of the examination, which is substantially reliant on human-operator skills. For bowel cancer, colonoscopy can miss up to 22% of dysplastic lesions, with even higher miss rates for small (<5 mm diameter) and flat lesions. We propose a robotic hyperspectral (HS) scanning framework that aims to improve the sensitivity of GI endoscopy by automated scanning and real-time classification of wide tissue areas based on their HS features. A “hot-spot” map is generated to highlight dysplastic or cancerous lesions for further scrutiny or concurrent resection. The device works as an add-on accessory to any conventional endoscope, and to our knowledge, is the first of its kind. This paper focuses on characterising its optical resolution on rigid and deformable colon phantoms. We report for the first time 2D and 3D wide-area reconstruction of endoscopic HS data with sub-millimetre optical resolution. The current setup, compatible with the anatomical dimensions of the colon, could allow the identification of flat and small pre-cancerous lesions that are currently missed. The proposed framework will lay the foundations towards the next generation of augmented reality endoscopy while increasing its sensitivity and specificity.
AU - Avila,Rencoret FB
AU - Elson,D
AU - Mylonas,G
PY - 2016///
TI - A Robotic Hyperspectral Scanning Framework for Endoscopy
UR - http://hdl.handle.net/10044/1/40268
ER -

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