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Cognitive Vision in Robotic Surgery

Head of Group

Dr Stamatia Giannarou

About us

The Cognitive Vision in Robotic Surgery Lab is developing computer vision and AI techniques for intraoperative navigation and real-time tissue characterisation.

What we do

Surgery is undergoing rapid changes driven by recent technological advances and our on-going pursuit towards early intervention and personalised treatment. We are developing computer vision and Artificial Intelligence techniques for intraoperative navigation and real-time tissue characterisation during minimally invasive and robot-assisted operations to improve both the efficacy and safety of surgical procedures. Our work will revolutionize the treatment of cancers and pave the way for autonomous robot-assisted interventions.

Why it is important?

With recent advances in medical imaging, sensing, and robotics, surgical oncology is entering a new era of early intervention, personalised treatment, and faster patient recovery. The main goal is to completely remove cancerous tissue while minimising damage to surrounding areas. However, achieving this can be challenging, often leading to imprecise surgeries, high re-excision rates, and reduced quality of life due to unintended injuries. Therefore, technologies that enhance cancer detection and enable more precise surgeries may improve patient outcomes.

How can it benefit patients?

Our methods aim to ensure patients receive accurate and timely surgical treatment while reducing surgeons' mental workload, overcoming limitations, and minimizing errors. By improving tumor excision, our hybrid diagnostic and therapeutic tools will lower recurrence rates and enhance survival outcomes. More complete tumor removal will also reduce the need for repeat procedures, improving patient quality of life, life expectancy, and benefiting society and the economy.

Meet the team

Dr Stamatia Giannarou
Senior Lecturer

Dr Po Wen Lo
Research Associate

Mr Alfie Roddan
Research Postgraduate

Mr Alistair G Weld
Research Postgraduate

Mr Chi Xu
Research Assistant

Mr Haozheng Xu
Research Assistant

Mr Yihang Zhou
Research Assistant

Citation

BibTex format

@article{Kassahun:2015:10.1007/s11548-015-1305-z,
author = {Kassahun, Y and Yu, B and Tibebu, AT and Stoyanov, D and Giannarou, S and Metzen, JH and Vander, Poorten E},
doi = {10.1007/s11548-015-1305-z},
journal = {International Journal of Computer Assisted Radiology and Surgery},
pages = {553--568},
title = {Surgical robotics beyond enhanced dexterity instrumentation: a survey of machine learning techniques and their role in intelligent and autonomous surgical actions},
url = {http://dx.doi.org/10.1007/s11548-015-1305-z},
volume = {11},
year = {2015}
}

Download

RIS format (EndNote, RefMan)

TY  - JOUR
AB  - PurposeAdvances in technology and computing play an increasingly important role in the evolution of modern surgical techniques and paradigms. This article reviews the current role of machine learning (ML) techniques in the context of surgery with a focus on surgical robotics (SR). Also, we provide a perspective on the future possibilities for enhancing the effectiveness of procedures by integrating ML in the operating room.MethodsThe review is focused on ML techniques directly applied to surgery, surgical robotics, surgical training and assessment. The widespread use of ML methods in diagnosis and medical image computing is beyond the scope of the review. Searches were performed on PubMed and IEEE Explore using combinations of keywords: ML, surgery, robotics, surgical and medical robotics, skill learning, skill analysis and learning to perceive.ResultsStudies making use of ML methods in the context of surgery are increasingly being reported. In particular, there is an increasing interest in using ML for developing tools to understand and model surgical skill and competence or to extract surgical workflow. Many researchers begin to integrate this understanding into the control of recent surgical robots and devices.ConclusionML is an expanding field. It is popular as it allows efficient processing of vast amounts of data for interpreting and real-time decision making. Already widely used in imaging and diagnosis, it is believed that ML will also play an important role in surgery and interventional treatments. In particular, ML could become a game changer into the conception of cognitive surgical robots. Such robots endowed with cognitive skills would assist the surgical team also on a cognitive level, such as possibly lowering the mental load of the team. For example, ML could help extracting surgical skill, learned through demonstration by human experts, and could transfer this to robotic skills. Such intelligent surgical assistance would significantly surpass the st
AU  - Kassahun,Y
AU  - Yu,B
AU  - Tibebu,AT
AU  - Stoyanov,D
AU  - Giannarou,S
AU  - Metzen,JH
AU  - Vander,Poorten E
DO  - 10.1007/s11548-015-1305-z
EP  - 568
PY  - 2015///
SN  - 1861-6410
SP  - 553
TI  - Surgical robotics beyond enhanced dexterity instrumentation: a survey of machine learning techniques and their role in intelligent and autonomous surgical actions
T2  - International Journal of Computer Assisted Radiology and Surgery
UR  - http://dx.doi.org/10.1007/s11548-015-1305-z
VL  - 11
ER  -