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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{Collins:2021:10.1016/j.euf.2021.04.006,
author = {Collins, JW and Marcus, HJ and Ghazi, A and Sridhar, A and Hashimoto, D and Hager, G and Arezzo, A and Jannin, P and Maier-Hein, L and Marz, K and Valdastri, P and Mori, K and Elson, D and Giannarou, S and Slack, M and Hares, L and Beaulieu, Y and Levy, J and Laplante, G and Ramadorai, A and Jarc, A and Andrews, B and Garcia, P and Neemuchwala, H and Andrusaite, A and Kimpe, T and Hawkes, D and Kelly, JD and Stoyanov, D},
doi = {10.1016/j.euf.2021.04.006},
journal = {European Urology Focus},
title = {Ethical implications of AI in robotic surgical training: A Delphi consensus statement},
url = {http://dx.doi.org/10.1016/j.euf.2021.04.006},
year = {2021}
}

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RIS format (EndNote, RefMan)

TY  - JOUR
AB  - ContextAs the role of AI in healthcare continues to expand there is increasing awareness of the potential pitfalls of AI and the need for guidance to avoid them.ObjectivesTo provide ethical guidance on developing narrow AI applications for surgical training curricula. We define standardised approaches to developing AI driven applications in surgical training that address current recognised ethical implications of utilising AI on surgical data. We aim to describe an ethical approach based on the current evidence, understanding of AI and available technologies, by seeking consensus from an expert committee.Evidence acquisitionThe project was carried out in 3 phases: (1) A steering group was formed to review the literature and summarize current evidence. (2) A larger expert panel convened and discussed the ethical implications of AI application based on the current evidence. A survey was created, with input from panel members. (3) Thirdly, panel-based consensus findings were determined using an online Delphi process to formulate guidance. 30 experts in AI implementation and/or training including clinicians, academics and industry contributed. The Delphi process underwent 3 rounds. Additions to the second and third-round surveys were formulated based on the answers and comments from previous rounds. Consensus opinion was defined as ≥ 80% agreement.Evidence synthesisThere was 100% response from all 3 rounds. The resulting formulated guidance showed good internal consistency, with a Cronbach alpha of >0.8. There was 100% consensus that there is currently a lack of guidance on the utilisation of AI in the setting of robotic surgical training. Consensus was reached in multiple areas, including: 1. Data protection and privacy; 2. Reproducibility and transparency; 3. Predictive analytics; 4. Inherent biases; 5. Areas of training most likely to benefit from AI.ConclusionsUsing the Delphi methodology, we achieved international consensus among experts to develop and reach
AU  - Collins,JW
AU  - Marcus,HJ
AU  - Ghazi,A
AU  - Sridhar,A
AU  - Hashimoto,D
AU  - Hager,G
AU  - Arezzo,A
AU  - Jannin,P
AU  - Maier-Hein,L
AU  - Marz,K
AU  - Valdastri,P
AU  - Mori,K
AU  - Elson,D
AU  - Giannarou,S
AU  - Slack,M
AU  - Hares,L
AU  - Beaulieu,Y
AU  - Levy,J
AU  - Laplante,G
AU  - Ramadorai,A
AU  - Jarc,A
AU  - Andrews,B
AU  - Garcia,P
AU  - Neemuchwala,H
AU  - Andrusaite,A
AU  - Kimpe,T
AU  - Hawkes,D
AU  - Kelly,JD
AU  - Stoyanov,D
DO  - 10.1016/j.euf.2021.04.006
PY  - 2021///
SN  - 2405-4569
TI  - Ethical implications of AI in robotic surgical training: A Delphi consensus statement
T2  - European Urology Focus
UR  - http://dx.doi.org/10.1016/j.euf.2021.04.006
UR  - https://www.sciencedirect.com/science/article/pii/S2405456921001127?via%3Dihub
ER  -