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Cognitive Vision in Robotic Surgery

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

Head of Group

Dr Stamatia (Matina) Giannarou

411 Bessemer Building
South Kensington Campus

+44 (0) 20 7594 8904

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 in Surgical Robot Vision

Mr Yihang Zhou
Research Assistant

Citation

BibTex format

@inbook{Davids:2022:10.1007/978-3-030-64573-1_30,
author = {Davids, J and Lam, K and Nimer, A and Gianarrou, S and Ashrafian, H},
booktitle = {Artificial Intelligence in Medicine},
doi = {10.1007/978-3-030-64573-1_30},
pages = {319--340},
title = {AIM in Medical Education},
url = {http://dx.doi.org/10.1007/978-3-030-64573-1_30},
year = {2022}
}

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RIS format (EndNote, RefMan)

TY  - CHAP
AB  - Artificial intelligence (AI) is making a global impact on various professions ranging from commerce to healthcare. This section looks at how it is beginning and will continue to impact other areas such as medical education. The multifaceted yet socrato-didactic methods of education need to evolve to cater for the twenty-firstcentury medical educator and trainee. Advances in machine learning and artificial intelligence are paving the way to new discoveries in medical education delivery. Methods This chapter begins by introducing the broad concepts of AI that are relevant to medical education and then addresses some of the emerging technologies employed to directly cater for aspects of medical education methodology and innovations to streamline education delivery, education assessments, and education policy. It then builds on this to further explore the nature of new artificial intelligence concepts for medical education delivery, educational assessments, and clinical education research discovery in a PRISMAguided systematic review and meta-analysis. Results Results from the meta-analysis showed improvement from using either AI alone or with conventional education methods compared to conventional methods alone. A significant pooled weighted mean difference ES estimate of ES 4.789; CI 1.9-7.67; p 1/4 0.001, I2 1/4 93% suggests a 479% learner improvement across domains of accuracy, sensitivity to performing educational tasks, and specificity. Significant amount of bias between studies was identified and a model to reduce bias is proposed. Conclusion AI in medical education shows considerable promise in domains of improving learners’ outcomes; this chapter rounds off its discussion with the role of AI in simulation methodologies and performance assessments for medical education, highlighting areas where it could augment how we deliver training.
AU  - Davids,J
AU  - Lam,K
AU  - Nimer,A
AU  - Gianarrou,S
AU  - Ashrafian,H
DO  - 10.1007/978-3-030-64573-1_30
EP  - 340
PY  - 2022///
SP  - 319
TI  - AIM in Medical Education
T1  - Artificial Intelligence in Medicine
UR  - http://dx.doi.org/10.1007/978-3-030-64573-1_30
ER  -

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