We use perceptual methods, AI, and frugal robotics innovation to deliver transformative diagnostic and treatment solutions.
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B415B Bessemer Building
South Kensington Campus
+44 (0)20 3312 5145
What we do
The HARMS lab leverages perceptually enabled methodologies, artificial intelligence, and frugal innovation in robotics (such as soft surgical robots) to deliver transformative solutions for diagnosis and treatment. Our research is driven by both problem-solving and curiosity, aiming to build a comprehensive understanding of the actions, interactions, and reactions occurring in the operating room. We focus on using robotic technologies to facilitate procedures that are not yet widely adopted, particularly in endoluminal surgery, such as advanced treatments for gastrointestinal cancer.
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Journal articleKinross JM, Mason SE, Mylonas G, et al., 2021,
Next-generation robotics in gastrointestinal surgery (vol 17, pg 430, 2020)
, NATURE REVIEWS GASTROENTEROLOGY & HEPATOLOGY, Vol: 18, Pages: 589-589, ISSN: 1759-5045 -
Journal articleEzzat A, Kogkas A, Holt J, et al., 2021,
An eye-tracking based robotic scrub nurse: proof of concept
, SURGICAL ENDOSCOPY AND OTHER INTERVENTIONAL TECHNIQUES, Vol: 35, Pages: 5381-5391, ISSN: 0930-2794BackgroundWithin surgery, assistive robotic devices (ARD) have reported improved patient outcomes. ARD can offer the surgical team a “third hand” to perform wider tasks and more degrees of motion in comparison with conventional laparoscopy. We test an eye-tracking based robotic scrub nurse (RSN) in a simulated operating room based on a novel real-time framework for theatre-wide 3D gaze localization in a mobile fashion.MethodsSurgeons performed segmental resection of pig colon and handsewn end-to-end anastomosis while wearing eye-tracking glasses (ETG) assisted by distributed RGB-D motion sensors. To select instruments, surgeons (ST) fixed their gaze on a screen, initiating the RSN to pick up and transfer the item. Comparison was made between the task with the assistance of a human scrub nurse (HSNt) versus the task with the assistance of robotic and human scrub nurse (R&HSNt). Task load (NASA-TLX), technology acceptance (Van der Laan’s), metric data on performance and team communication were measured.ResultsOverall, 10 ST participated. NASA-TLX feedback for ST on HSNt vs R&HSNt usage revealed no significant difference in mental, physical or temporal demands and no change in task performance. ST reported significantly higher frustration score with R&HSNt. Van der Laan’s scores showed positive usefulness and satisfaction scores in using the RSN. No significant difference in operating time was observed.ConclusionsWe report initial findings of our eye-tracking based RSN. This enables mobile, unrestricted hands-free human–robot interaction intra-operatively. Importantly, this platform is deemed non-inferior to HSNt and accepted by ST and HSN test users.
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Journal articleSivananthan A, Kogkas A, Glover B, et al., 2021,
A novel gaze-controlled flexible robotized endoscope; preliminary trial and report
, SURGICAL ENDOSCOPY AND OTHER INTERVENTIONAL TECHNIQUES, Vol: 35, Pages: 4890-4899, ISSN: 0930-2794BackgroundInterventional endoluminal therapy is rapidly advancing as a minimally invasive surgical technique. The expanding remit of endoscopic therapy necessitates precision control. Eye tracking is an emerging technology which allows intuitive control of devices. This was a feasibility study to establish if a novel eye gaze-controlled endoscopic system could be used to intuitively control an endoscope.MethodsAn eye gaze-control system consisting of eye tracking glasses, specialist cameras and a joystick was used to control a robotically driven endoscope allowing steering, advancement, withdrawal and retroflexion. Eight experienced and eight non-endoscopists used both the eye gaze system and a conventional endoscope to identify ten targets in two simulated environments: a sphere and an upper gastrointestinal (UGI) model.Completion of tasks was timed. Subjective feedback was collected from each participant on task load (NASA Task Load Index) and acceptance of technology (Van der Laan scale).ResultsWhen using gaze-control endoscopy, non-endoscopists were significantly quicker when using gaze-control rather than conventional endoscopy (sphere task 3:54 ± 1:17 vs. 9:05 ± 5:40 min, p = 0.012, and UGI model task 1:59 ± 0:24 vs 3:45 ± 0:53 min, p < .001).Non-endoscopists reported significantly higher NASA-TLX workload total scores using conventional endoscopy versus gaze-control (80.6 ± 11.3 vs 22.5 ± 13.8, p < .001). Endoscopists reported significantly higher total NASA-TLX workload scores using gaze control versus conventional endoscopy (54.2 ± 16 vs 26.9 ± 15.3, p = 0.012). All subjects reported that the gaze-control had positive ‘usefulness’ and ‘satisfaction’ score of 0.56 ± 0.83 and 1.43 &
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Conference paperKhan DZ, Kafai Golahmadi A, Mylonas G, et al., 2021,
662 Tool-tissue Forces in Surgery: A Systematic Review
, ASiT/MedAll Virtual Surgical Summit, Publisher: OXFORD UNIV PRESS, ISSN: 0007-1323 -
Conference paperAshraf H, Sodergren M, Mylonas G, et al., 2021,
837 The Identification of Gaze Behaviour and Physiological Markers Associated With Making An Error During Laparoscopic Cholecystectomy
, ASiT/MedAll Virtual Surgical Summit, Publisher: OXFORD UNIV PRESS, ISSN: 0007-1323 -
Journal articleGolahmadi AK, Khan DZ, Mylonas GP, et al., 2021,
Tool-tissue forces in surgery: A systematic review
, Annals of Medicine and Surgery, Vol: 65, Pages: 1-7, ISSN: 2049-0801BackgroundExcessive tool-tissue interaction forces often result in tissue damage and intraoperative complications, while insufficient forces prevent the completion of the task. This review sought to explore the tool-tissue interaction forces exerted by instruments during surgery across different specialities, tissues, manoeuvres and experience levels.Materials & methodsA PRISMA-guided systematic review was carried out using Embase, Medline and Web of Science databases.ResultsOf 462 articles screened, 45 studies discussing surgical tool-tissue forces were included. The studies were categorized into 9 different specialities with the mean of average forces lowest for ophthalmology (0.04N) and highest for orthopaedic surgery (210N). Nervous tissue required the least amount of force to manipulate (mean of average: 0.4N), whilst connective tissue (including bone) required the most (mean of average: 45.8). For manoeuvres, drilling recorded the highest forces (mean of average: 14N), whilst sharp dissection recorded the lowest (mean of average: 0.03N). When comparing differences in the mean of average forces between groups, novices exerted 22.7% more force than experts, and presence of a feedback mechanism (e.g. audio) reduced exerted forces by 47.9%.ConclusionsThe measurement of tool-tissue forces is a novel but rapidly expanding field. The range of forces applied varies according to surgical speciality, tissue, manoeuvre, operator experience and feedback provided. Knowledge of the safe range of surgical forces will improve surgical safety whilst maintaining effectiveness. Measuring forces during surgery may provide an objective metric for training and assessment. Development of smart instruments, robotics and integrated feedback systems will facilitate this.
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Journal articleSaracino A, Oude-Vrielink TJC, Menciassi A, et al., 2020,
Haptic Intracorporeal Palpation Using a Cable-Driven Parallel Robot: A User Study
, IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING, Vol: 67, Pages: 3452-3463, ISSN: 0018-9294- Author Web Link
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Journal articleArezzo A, Francis N, Mintz Y, et al., 2020,
EAES recommendations for recovery plan in minimally invasive surgery amid COVID-19 pandemic
, SURGICAL ENDOSCOPY AND OTHER INTERVENTIONAL TECHNIQUES, Vol: 35, Pages: 1-17, ISSN: 0930-2794BackgroundCOVID-19 pandemic presented an unexpected challenge for the surgical community in general and Minimally Invasive Surgery (MIS) specialists in particular. This document aims to summarize recent evidence and experts’ opinion and formulate recommendations to guide the surgical community on how to best organize the recovery plan for surgical activity across different sub-specialities after the COVID-19 pandemic.MethodsRecommendations were developed through a Delphi process for establishment of expert consensus. Domain topics were formulated and subsequently subdivided into questions pertinent to different surgical specialities following the COVID-19 crisis. Sixty-five experts from 24 countries, representing the entire EAES board, were invited. Fifty clinicians and six engineers accepted the invitation and drafted statements based on specific key questions. Anonymous voting on the statements was performed until consensus was achieved, defined by at least 70% agreement.ResultsA total of 92 consensus statements were formulated with regard to safe resumption of surgery across eight domains, addressing general surgery, upper GI, lower GI, bariatrics, endocrine, HPB, abdominal wall and technology/research. The statements addressed elective and emergency services across all subspecialties with specific attention to the role of MIS during the recovery plan. Eighty-four of the statements were approved during the first round of Delphi voting (91.3%) and another 8 during the following round after substantial modification, resulting in a 100% consensus.ConclusionThe recommendations formulated by the EAES board establish a framework for resumption of surgery following COVID-19 pandemic with particular focus on the role of MIS across surgical specialities. The statements have the potential for wide application in the clinical setting, education activities and research work across different healthcare systems.
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Journal articleAvery J, Shulakova D, Runciman M, et al., 2020,
Tactile sensor for minimally invasive surgery using Electrical Impedance Tomography
, IEEE Transactions on Medical Robotics and Bionics, Vol: 2, Pages: 561-564, ISSN: 2576-3202Whilst offering numerous benefits to patients, minimally invasive surgery (MIS) has a disadvantage in the loss of tactile feedback to the surgeon, traditionally offering valuable qualitative tissue assessment, such as tumour identification and localisation. Tactile sensors aim to overcome this loss of sensation by detecting tissue characteristics such as stiffness, composition and temperature. Tactile sensors have previously been incorporated into MIS robotic end effectors, which require lengthy scanning procedures due to localised sensitivity. Distributed tactile sensors, or “artificial skin” offer a map of tissue properties in a single instance but are often not suitable for MIS applications due to limited biocompatibility or large collapsed volumes. We propose a deployable, soft, tactile sensor with a deformable saline chamber and integrated Electrical Impedance Tomography (EIT) electrodes. During contact with tissue, the saline is displaced from the chamber and the lesion size and stiffness can be inferred from the resultant impedance changes. Through optimisation of the EIT measurement protocol and hardware the sensor was capable of localising the centre of mass of palpation targets within 1.5 mm in simulation and 2.3–4.6mm in phantom experiments. Reconstructed image metrics differentiated target objects from 8–30 mm.
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Journal articleCursi F, Mylonas GP, Kormushev P, 2020,
Adaptive kinematic modelling for multiobjective control of a redundant surgical robotic tool
, Robotics, Vol: 9, Pages: 68-68, ISSN: 2218-6581Accurate kinematic models are essential for effective control of surgical robots. For tendon driven robots, which are common for minimally invasive surgery, the high nonlinearities in the transmission make modelling complex. Machine learning techniques are a preferred approach to tackle this problem. However, surgical environments are rarely structured, due to organs being very soft and deformable, and unpredictable, for instance, because of fluids in the system, wear and break of the tendons that lead to changes of the system’s behaviour. Therefore, the model needs to quickly adapt. In this work, we propose a method to learn the kinematic model of a redundant surgical robot and control it to perform surgical tasks both autonomously and in teleoperation. The approach employs Feedforward Artificial Neural Networks (ANN) for building the kinematic model of the robot offline, and an online adaptive strategy in order to allow the system to conform to the changing environment. To prove the capabilities of the method, a comparison with a simple feedback controller for autonomous tracking is carried out. Simulation results show that the proposed method is capable of achieving very small tracking errors, even when unpredicted changes in the system occur, such as broken joints. The method proved effective also in guaranteeing accurate tracking in teleoperation.
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Bessemer Building
South Kensington Campus
Imperial College
London, SW7 2AZ
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