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Mechatronics in Medicine | MiM Lab

The MIM Lab develops robotic and mechatronics surgical systems for a variety of procedures.

Head of Group

B415C Bessemer Building
South Kensington Campus

+44 (0)20 7594 7046

What we do

The Mechatronics in Medicine Laboratory develops robotic and mechatronics surgical systems for a variety of procedures including neuro, cardiovascular, orthopaedic surgeries, and colonoscopies. Examples include bio-inspired catheters that can navigate along complex paths within the brain (such as EDEN2020), soft robots to explore endoluminal anatomies (such as the colon), and virtual reality solutions to support surgeons during knee replacement surgeries.

Why is it important

The integration of mechatronics into medicine addresses critical challenges in modern healthcare by enhancing the precision, safety, and efficiency of surgical procedures. Traditional surgeries often involve significant risks and extended recovery times. By developing robotic systems that offer greater accuracy and control, we aim to minimise these risks and reduce invasiveness. Our research contributes to the advancement of minimally invasive techniques, which are essential for improving patient outcomes and optimising healthcare resources. Furthermore, our work supports the training of the next generation of surgeons, equipping them with cutting-edge tools and methodologies that reflect the evolving landscape of medical technology.

How can it benefit patients

Patients stand to gain significantly from the innovations developed at the Mechatronics in Medicine Laboratory. Our robotic systems are designed to perform surgeries with enhanced precision, leading to fewer complications and faster recovery times. Minimally invasive procedures facilitated by our technologies result in less postoperative pain and reduced scarring, improving the overall patient experience. Additionally, the increased accuracy of our systems can lead to better surgical outcomes, such as more complete tumour removals or more precise joint replacements, thereby improving long-term health prospects. By pushing the boundaries of medical robotics, we strive to make advanced surgical care more accessible and effective for patients worldwide.

Meet the team

Dr Daniel Bautista Salinas
Research Associate

Dr Kaiwen Chen
Research Associate

Dr Zejian Cui
Research Associate

Mr Connor Daly
Research Postgraduate

Emeritus Professor Brian L Davies FREng
Emeritus Professor

Mr Zhaoyang Jacopo Hu
Research Postgraduate

Dr Hisham M Iqbal
Research Associate

Mr Alex Ranne
Research Postgraduate

Professor Ferdinando M Rodriguez y Baena
Co-Director of Hamlyn Centre, Professor of Medical Robotics

Dr Jialei Shi
Research Associate

Mr Spyridon Souipas
Casual - Other work

Ms Emilia Zari
Research Postgraduate

Citation

BibTex format

@article{Hu:2024:10.1109/TAI.2024.3429048,
author = {Hu, X and Cutolo, F and Iqbal, H and Henckel, J and Rodriguez, y Baena F},
doi = {10.1109/TAI.2024.3429048},
journal = {IEEE Transactions on Artificial Intelligence},
title = {Artificial Intelligence-driven Framework for Augmented Reality Markerless Navigation in Knee Surgery},
url = {http://dx.doi.org/10.1109/TAI.2024.3429048},
year = {2024}
}

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RIS format (EndNote, RefMan)

TY  - JOUR
AB  - Conventional orthopaedic navigation systems depend on marker-based tracking, which may introduce additional skin incisions, increase the risk and discomfort for the patient, and entail increased workflow complexity. The guidance is conveyed via 2D monitors, which may distract the surgeon and increase the cognitive burden.</p> <p>This study presents an Artificial Intelligence (AI) - driven surgical navigation framework for knee replacement surgery. The system comprises an Augmented Reality (AR) interface that combines an occlusions-robust deep learning-based markerless bone tracking and registration algorithm with a commercial HoloLens 2 headset calibrated for the user’s perspective on both eyes. The feasibility of such a system in navigating a bone drilling task is investigated with an experienced orthopaedic surgeon on three cadaveric knees under realistic operating room conditions. After registering an implant model to computed tomography (CT) scans, the preoperative plans are determined based on the location of the fixation pins. Navigation accuracy is quantified using a highly accurate optical tracking system.</p> <p>The achieved drilling error is 7.88±2.41mm in translation and 7.36±1.77° in orientation. The results demonstrate the viability of integrating AI and AR technology to navigate knee surgery.
AU  - Hu,X
AU  - Cutolo,F
AU  - Iqbal,H
AU  - Henckel,J
AU  - Rodriguez,y Baena F
DO  - 10.1109/TAI.2024.3429048
PY  - 2024///
TI  - Artificial Intelligence-driven Framework for Augmented Reality Markerless Navigation in Knee Surgery
T2  - IEEE Transactions on Artificial Intelligence
UR  - http://dx.doi.org/10.1109/TAI.2024.3429048
ER  -

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