In this section
The Cognitive Vision in Robotic Surgery Lab is developing computer vision and AI techniques for intraoperative navigation and real-time tissue characterisation.
Dr Stamatia (Matina) Giannarou
411 Bessemer Building
South Kensington Campus
+44 (0) 20 7594 8904
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.
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.
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.
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
@article{Davids:2020:10.1007/s10143-020-01378-0,
author = {Davids, J and Manivannan, S and Darzi, A and Giannarou, S and Ashrafian, H and Marcus, HJ},
doi = {10.1007/s10143-020-01378-0},
journal = {Neurosurgical Review},
pages = {1853--1867},
title = {Simulation for skills training in neurosurgery: a systematic review, meta-analysis, and analysis of progressive scholarly acceptance.},
url = {http://dx.doi.org/10.1007/s10143-020-01378-0},
volume = {44},
year = {2020}
}
TY - JOUR
AB - At a time of significant global unrest and uncertainty surrounding how the delivery of clinical training will unfold over the coming years, we offer a systematic review, meta-analysis, and bibliometric analysis of global studies showing the crucial role simulation will play in training. Our aim was to determine the types of simulators in use, their effectiveness in improving clinical skills, and whether we have reached a point of global acceptance. A PRISMA-guided global systematic review of the neurosurgical simulators available, a meta-analysis of their effectiveness, and an extended analysis of their progressive scholarly acceptance on studies meeting our inclusion criteria of simulation in neurosurgical education were performed. Improvement in procedural knowledge and technical skills was evaluated. Of the identified 7405 studies, 56 studies met the inclusion criteria, collectively reporting 50 simulator types ranging from cadaveric, low-fidelity, and part-task to virtual reality (VR) simulators. In all, 32 studies were included in the meta-analysis, including 7 randomised controlled trials. A random effects, ratio of means effects measure quantified statistically significant improvement in procedural knowledge by 50.2% (ES 0.502; CI 0.355; 0.649, p < 0.001), technical skill including accuracy by 32.5% (ES 0.325; CI - 0.482; - 0.167, p < 0.001), and speed by 25% (ES - 0.25, CI - 0.399; - 0.107, p < 0.001). The initial number of VR studies (n = 91) was approximately double the number of refining studies (n = 45) indicating it is yet to reach progressive scholarly acceptance. There is strong evidence for a beneficial impact of adopting simulation in the improvement of procedural knowledge and technical skill. We show a growing trend towards the adoption of neurosurgical simulators, although we have not fully gained progressive scholarly acceptance for VR-based simulation technologies in neurosurgical education.
AU - Davids,J
AU - Manivannan,S
AU - Darzi,A
AU - Giannarou,S
AU - Ashrafian,H
AU - Marcus,HJ
DO - 10.1007/s10143-020-01378-0
EP - 1867
PY - 2020///
SN - 0344-5607
SP - 1853
TI - Simulation for skills training in neurosurgery: a systematic review, meta-analysis, and analysis of progressive scholarly acceptance.
T2 - Neurosurgical Review
UR - http://dx.doi.org/10.1007/s10143-020-01378-0
UR - https://www.ncbi.nlm.nih.gov/pubmed/32944808
UR - https://link.springer.com/article/10.1007%2Fs10143-020-01378-0
UR - http://hdl.handle.net/10044/1/82759
VL - 44
ER -
The Hamlyn Centre
Bessemer Building
South Kensington Campus
Imperial College
London, SW7 2AZ
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