The impact of the size and angle of the cochlear basal turn on translocation of a pre-curved mid-scala cochlear implant electrode.

Scalar translocation is a severe form of intra-cochlear trauma during cochlear implant (CI) electrode insertion. This study explored the hypothesis that the dimensions of the cochlear basal turn and orientation of its inferior segment relative to surgically relevant anatomical structures influence the scalar translocation rates of a pre-curved CI electrode. In a cohort of 40 patients implanted with the Advanced Bionics Mid-Scala electrode array, the scalar translocation group (40%) had a significantly smaller mean distance A of the cochlear basal turn (p < 0.001) and wider horizontal angle between the inferior segment of the cochlear basal turn and the mastoid facial nerve (p = 0.040). A logistic regression model incorporating distance A (p = 0.003) and horizontal facial nerve angle (p = 0.017) explained 44.0-59.9% of the variance in scalar translocation and correctly classified 82.5% of cases. Every 1mm decrease in distance A was associated with a 99.2% increase in odds of translocation [95% confidence interval 80.3%, 100%], whilst every 1-degree increase in the horizontal facial nerve angle was associated with an 18.1% increase in odds of translocation [95% CI 3.0%, 35.5%]. The study findings provide an evidence-based argument for the development of a navigation system for optimal angulation of electrode insertion during CI surgery to reduce intra-cochlear trauma.

Trocar localisation for robot-assisted vitreoretinal surgery.

PURPOSE: Robot-assisted vitreoretinal surgery provides precise and consistent operations on the back of the eye. To perform this safely, knowledge of the surgical instrument's remote centre of motion (RCM) and the location of the insertion point into the eye (trocar) is required. This enables the robot to align both positions to pivot the instrument about the trocar, thus preventing any damaging lateral forces from being exerted. METHODS: Building on a system developed in previous work, this study presents a trocar localisation method that uses a micro-camera mounted on a vitreoretinal surgical forceps, to track two ArUco markers attached on either side of a trocar. The trocar position is the estimated midpoint between the markers. RESULTS: Experimental evaluation of the trocar localisation was conducted. Results showed an RMSE of 1.82 mm for the localisation of the markers and an RMSE of 1.24 mm for the trocar localisation. CONCLUSIONS: The proposed camera-based trocar localisation presents reasonable consistency and accuracy and shows improved results compared to other current methods. Optimum accuracy for this application would necessitate a 1.4 mm absolute error margin, which corresponds to the trocar's radius. The trocar localisation results are successfully found within this margin, yet the marker localisation would require further refinement to ensure consistency of localisation within the error margin. Further work will refine these position estimates and ensure the error stays consistently within this boundary.

Comparative verification of control methodology for robotic interventional neuroradiology procedures.

PURPOSE: The use of robotics is emerging for performing interventional radiology procedures. Robots in interventional radiology are typically controlled using button presses and joystick movements. This study identified how different human-robot interfaces affect endovascular surgical performance using interventional radiology simulations. METHODS: Nine participants performed a navigation task on an interventional radiology simulator with three different human-computer interfaces. Using Simulation Open Framework Architecture we developed a simulation profile of vessels, catheters and guidewires. We designed and manufactured a bespoke haptic interventional radiology controller for robotic systems to control the simulation. Metrics including time taken for navigation, number of incorrect catheterisations, number of catheter and guidewire prolapses and forces applied to vessel walls were measured and used to characterise the interfaces. Finally, participants responded to a questionnaire to evaluate the perception of the controllers. RESULTS: Time taken for navigation, number of incorrect catheterisations and the number of catheter and guidewire prolapses, showed that the device-mimicking controller is better suited for controlling interventional neuroradiology procedures over joystick control approaches. Qualitative metrics also showed that interventional radiologists prefer a device-mimicking controller approach over a joystick approach. CONCLUSION: Of the four metrics used to compare and contrast the human-robot interfaces, three conclusively showed that a device-mimicking controller was better suited for controlling interventional neuroradiology robotics.

Towards A Physics-based Model for Steerable Eversion Growing Robots.

Soft robots that grow through eversion/apical extension can effectively navigate fragile environments such as ducts and vessels inside the human body. This paper presents the physics-based model of a miniature steerable eversion growing robot. We demonstrate the robot's growing, steering, stiffening and interaction capabilities. The interaction between two robot-internal components is explored, i.e., a steerable catheter for robot tip orientation, and a growing sheath for robot elongation/retraction. The behavior of the growing robot under different inner pressures and external tip forces is investigated. Simulations are carried out within the SOFA framework. Extensive experimentation with a physical robot setup demonstrates agreement with the simulations. The comparison demonstrates a mean absolute error of 10 - 20% between simulation and experimental results for curvature values, including catheter-only experiments, sheath-only experiments and full system experiments. To our knowledge, this is the first work to explore physics-based modelling of a tendon-driven steerable eversion growing robot. While our work is motivated by early breast cancer detection through mammary duct inspection and uses our MAMMOBOT robot prototype, our approach is general and relevant to similar growing robots.

Using deep learning to detect diabetic retinopathy on handheld non-mydriatic retinal images acquired by field workers in community settings.

Diabetic retinopathy (DR) at risk of vision loss (referable DR) needs to be identified by retinal screening and referred to an ophthalmologist. Existing automated algorithms have mostly been developed from images acquired with high cost mydriatic retinal cameras and cannot be applied in the settings used in most low- and middle-income countries. In this prospective multicentre study, we developed a deep learning system (DLS) that detects referable DR from retinal images acquired using handheld non-mydriatic fundus camera by non-technical field workers in 20 sites across India. Macula-centred and optic-disc-centred images from 16,247 eyes (9778 participants) were used to train and cross-validate the DLS and risk factor based logistic regression models. The DLS achieved an AUROC of 0.99 (1000 times bootstrapped 95% CI 0.98-0.99) using two-field retinal images, with 93.86 (91.34-96.08) sensitivity and 96.00 (94.68-98.09) specificity at the Youden's index operational point. With single field inputs, the DLS reached AUROC of 0.98 (0.98-0.98) for the macula field and 0.96 (0.95-0.98) for the optic-disc field. Intergrader performance was 90.01 (88.95-91.01) sensitivity and 96.09 (95.72-96.42) specificity. The image based DLS outperformed all risk factor-based models. This DLS demonstrated a clinically acceptable performance for the identification of referable DR despite challenging image capture conditions.

Semiautonomous Robotic Manipulator for Minimally Invasive Aortic Valve Replacement.

Aortic valve surgery is the preferred procedure for replacing a damaged valve with an artificial one. The ValveTech robotic platform comprises a flexible articulated manipulator and surgical interface supporting the effective delivery of an artificial valve by teleoperation and endoscopic vision. This article presents our recent work on force-perceptive, safe, semiautonomous navigation of the ValveTech platform prior to valve implantation. First, we present a force observer that transfers forces from the manipulator body and tip to a haptic interface. Second, we demonstrate how hybrid forward/inverse mechanics, together with endoscopic visual servoing, lead to autonomous valve positioning. Benchtop experiments and an artificial phantom quantify the performance of the developed robot controller and navigator. Valves can be autonomously delivered with a 2.0±0.5 mm position error and a minimal misalignment of 3.4±0.9°. The hybrid force/shape observer (FSO) algorithm was able to predict distributed external forces on the articulated manipulator body with an average error of 0.09 N. FSO can also estimate loads on the tip with an average accuracy of 3.3%. The presented system can lead to better patient care, delivery outcome, and surgeon comfort during aortic valve surgery, without requiring sensorization of the robot tip, and therefore obviating miniaturization constraints.

Automated image curation in diabetic retinopathy screening using deep learning.

Diabetic retinopathy (DR) screening images are heterogeneous and contain undesirable non-retinal, incorrect field and ungradable samples which require curation, a laborious task to perform manually. We developed and validated single and multi-output laterality, retinal presence, retinal field and gradability classification deep learning (DL) models for automated curation. The internal dataset comprised of 7743 images from DR screening (UK) with 1479 external test images (Portugal and Paraguay). Internal vs external multi-output laterality AUROC were right (0.994 vs 0.905), left (0.994 vs 0.911) and unidentifiable (0.996 vs 0.680). Retinal presence AUROC were (1.000 vs 1.000). Retinal field AUROC were macula (0.994 vs 0.955), nasal (0.995 vs 0.962) and other retinal field (0.997 vs 0.944). Gradability AUROC were (0.985 vs 0.918). DL effectively detects laterality, retinal presence, retinal field and gradability of DR screening images with generalisation between centres and populations. DL models could be used for automated image curation within DR screening.

Surgical biomicroscopy-guided intra-operative optical coherence tomography (iOCT) image super-resolution.

PURPOSE: Intra-retinal delivery of novel sight-restoring therapies will require the precision of robotic systems accompanied by excellent visualisation of retinal layers. Intra-operative Optical Coherence Tomography (iOCT) provides cross-sectional retinal images in real time but at the cost of image quality that is insufficient for intra-retinal therapy delivery.This paper proposes a super-resolution methodology that improves iOCT image quality leveraging spatiotemporal consistency of incoming iOCT video streams. METHODS: To overcome the absence of ground truth high-resolution (HR) images, we first generate HR iOCT images by fusing spatially aligned iOCT video frames. Then, we automatically assess the quality of the HR images on key retinal layers using a deep semantic segmentation model. Finally, we use image-to-image translation models (Pix2Pix and CycleGAN) to enhance the quality of LR images via quality transfer from the estimated HR domain. RESULTS: Our proposed methodology generates iOCT images of improved quality according to both full-reference and no-reference metrics. A qualitative study with expert clinicians also confirms the improvement in the delineation of pertinent layers and in the reduction of artefacts. Furthermore, our approach outperforms conventional denoising filters and the learning-based state-of-the-art. CONCLUSIONS: The results indicate that the learning-based methods using the estimated, through our pipeline, HR domain can be used to enhance the iOCT image quality. Therefore, the proposed method can computationally augment the capabilities of iOCT imaging helping this modality support the vitreoretinal surgical interventions of the future.

OxVent: Design and evaluation of a rapidly-manufactured Covid-19 ventilator.

BACKGROUND: The manufacturing of any standard mechanical ventilator cannot rapidly be upscaled to several thousand units per week, largely due to supply chain limitations. The aim of this study was to design, verify and perform a pre-clinical evaluation of a mechanical ventilator based on components not required for standard ventilators, and that met the specifications provided by the Medicines and Healthcare Products Regulatory Agency (MHRA) for rapidly-manufactured ventilator systems (RMVS). METHODS: The design utilises closed-loop negative feedback control, with real-time monitoring and alarms. Using a standard test lung, we determined the difference between delivered and target tidal volume (VT) at respiratory rates between 20 and 29 breaths per minute, and the ventilator's ability to deliver consistent VT during continuous operation for >14 days (RMVS specification). Additionally, four anaesthetised domestic pigs (3 male-1 female) were studied before and after lung injury to provide evidence of the ventilator's functionality, and ability to support spontaneous breathing. FINDINGS: Continuous operation lasted 23 days, when the greatest difference between delivered and target VT was 10% at inspiratory flow rates >825 mL/s. In the pre-clinical evaluation, the VT difference was -1 (-90 to 88) mL [mean (LoA)], and positive end-expiratory pressure (PEEP) difference was -2 (-8 to 4) cmH2O. VT delivery being triggered by pressures below PEEP demonstrated spontaneous ventilation support. INTERPRETATION: The mechanical ventilator presented meets the MHRA therapy standards for RMVS and, being based on largely available components, can be manufactured at scale. FUNDING: Work supported by Wellcome/EPSRC Centre for Medical Engineering,King's Together Fund and Oxford University.

Deep homography estimation in dynamic surgical scenes for laparoscopic camera motion extraction.

Current laparoscopic camera motion automation relies on rule-based approaches or only focuses on surgical tools. Imitation Learning (IL) methods could alleviate these shortcomings, but have so far been applied to oversimplified setups. Instead of extracting actions from oversimplified setups, in this work we introduce a method that allows to extract a laparoscope holder's actions from videos of laparoscopic interventions. We synthetically add camera motion to a newly acquired dataset of camera motion free da Vinci surgery image sequences through a novel homography generation algorithm. The synthetic camera motion serves as a supervisory signal for camera motion estimation that is invariant to object and tool motion. We perform an extensive evaluation of state-of-the-art (SOTA) Deep Neural Networks (DNNs) across multiple compute regimes, finding our method transfers from our camera motion free da Vinci surgery dataset to videos of laparoscopic interventions, outperforming classical homography estimation approaches in both, precision by 41%, and runtime on a CPU by 43%.

Robotics in neurointerventional surgery: a systematic review of the literature.

BACKGROUND: Robotically performed neurointerventional surgery has the potential to reduce occupational hazards to staff, perform intervention with greater precision, and could be a viable solution for teleoperated neurointerventional procedures. OBJECTIVE: To determine the indication, robotic systems used, efficacy, safety, and the degree of manual assistance required for robotically performed neurointervention. METHODS: We conducted a systematic review of the literature up to, and including, articles published on April 12, 2021. Medline, PubMed, Embase, and Cochrane register databases were searched using medical subject heading terms to identify reports of robotically performed neurointervention, including diagnostic cerebral angiography and carotid artery intervention. RESULTS: A total of 8 articles treating 81 patients were included. Only one case report used a robotic system for intracranial intervention, the remaining indications being cerebral angiography and carotid artery intervention. Only one study performed a comparison of robotic and manual procedures. Across all studies, the technical success rate was 96% and the clinical success rate was 100%. All cases required a degree of manual assistance. No studies had clearly defined patient selection criteria, reference standards, or index tests, preventing meaningful statistical analysis. CONCLUSIONS: Given the clinical success, it is plausible that robotically performed neurointerventional procedures will eventually benefit patients and reduce occupational hazards for staff; however, there is no high-level efficacy and safety evidence to support this assertion. Limitations of current robotic systems and the challenges that must be overcome to realize the potential for remote teleoperated neurointervention require further investigation.

Gripe-Needle: A Sticky Suction Cup Gripper Equipped Needle for Targeted Therapeutics Delivery.

This paper presents a multi-purpose gripping and incision tool-set to reduce the number of required manipulators for targeted therapeutics delivery in Minimally Invasive Surgery. We have recently proposed the use of multi-arm Concentric Tube Robots (CTR) consisting of an incision, a camera, and a gripper manipulator for deep orbital interventions, with a focus on Optic Nerve Sheath Fenestration (ONSF). The proposed prototype in this research, called Gripe-Needle, is a needle equipped with a sticky suction cup gripper capable of performing both gripping of target tissue and incision tasks in the optic nerve area by exploiting the multi-tube arrangement of a CTR for actuation of the different tool-set units. As a result, there will be no need for an independent gripper arm for an incision task. The CTR innermost tube is equipped with a needle, providing the pathway for drug delivery, and the immediate outer tube is attached to the suction cup, providing the suction pathway. Based on experiments on various materials, we observed that adding a sticky surface with bio-inspired grooves to a normal suction cup gripper has many advantages such as, 1) enhanced adhesion through material stickiness and by air-tightening the contact surface, 2) maintained adhesion despite internal pressure variations, e.g. due to the needle motion, and 3) sliding resistance. Simple Finite Element and theoretical modeling frameworks are proposed, based on which a miniature tool-set is designed to achieve the required gripping forces during ONSF. The final designs were successfully tested for accessing the optic nerve of a realistic eye phantom in a skull eye orbit, robust gripping and incision on units of a plastic bubble wrap sample, and manipulating different tissue types of porcine eye samples.

Deep learning for gradability classification of handheld, non-mydriatic retinal images.

Screening effectively identifies patients at risk of sight-threatening diabetic retinopathy (STDR) when retinal images are captured through dilated pupils. Pharmacological mydriasis is not logistically feasible in non-clinical, community DR screening, where acquiring gradable retinal images using handheld devices exhibits high technical failure rates, reducing STDR detection. Deep learning (DL) based gradability predictions at acquisition could prompt device operators to recapture insufficient quality images, increasing gradable image proportions and consequently STDR detection. Non-mydriatic retinal images were captured as part of SMART India, a cross-sectional, multi-site, community-based, house-to-house DR screening study between August 2018 and December 2019 using the Zeiss Visuscout 100 handheld camera. From 18,277 patient eyes (40,126 images), 16,170 patient eyes (35,319 images) were eligible and 3261 retinal images (1490 patient eyes) were sampled then labelled by two ophthalmologists. Compact DL model area under the receiver operator characteristic curve was 0.93 (0.01) following five-fold cross-validation. Compact DL model agreement (Kappa) were 0.58, 0.69 and 0.69 for high specificity, balanced sensitivity/specificity and high sensitivity operating points compared to an inter-grader agreement of 0.59. Compact DL gradability model performance was favourable compared to ophthalmologists. Compact DL models can effectively classify non-mydriatic, handheld retinal image gradability with potential applications within community-based DR screening.

Deep iterative vessel segmentation in OCT angiography.

This paper addresses retinal vessel segmentation on optical coherence tomography angiography (OCT-A) images of the human retina. Our approach is motivated by the need for high precision image-guided delivery of regenerative therapies in vitreo-retinal surgery. OCT-A visualizes macular vasculature, the main landmark of the surgically targeted area, at a level of detail and spatial extent unattainable by other imaging modalities. Thus, automatic extraction of detailed vessel maps can ultimately inform surgical planning. We address the task of delineation of the Superficial Vascular Plexus in 2D Maximum Intensity Projections (MIP) of OCT-A using convolutional neural networks that iteratively refine the quality of the produced vessel segmentations. We demonstrate that the proposed approach compares favourably to alternative network baselines and graph-based methodologies through extensive experimental analysis, using data collected from 50 subjects, including both individuals that underwent surgery for structural macular abnormalities and healthy subjects. Additionally, we demonstrate generalization to 3D segmentation and narrower field-of-view OCT-A. In the future, the extracted vessel maps will be leveraged for surgical planning and semi-automated intraoperative navigation in vitreo-retinal surgery.

Learned optical flow for intra-operative tracking of the retinal fundus.

PURPOSE: Sustained delivery of regenerative retinal therapies by robotic systems requires intra-operative tracking of the retinal fundus. We propose a supervised deep convolutional neural network to densely predict semantic segmentation and optical flow of the retina as mutually supportive tasks, implicitly inpainting retinal flow information missing due to occlusion by surgical tools. METHODS: As manual annotation of optical flow is infeasible, we propose a flexible algorithm for generation of large synthetic training datasets on the basis of given intra-operative retinal images. We evaluate optical flow estimation by tracking a grid and sparsely annotated ground truth points on a benchmark of challenging real intra-operative clips obtained from an extensive internally acquired dataset encompassing representative vitreoretinal surgical cases. RESULTS: The U-Net-based network trained on the synthetic dataset is shown to generalise well to the benchmark of real surgical videos. When used to track retinal points of interest, our flow estimation outperforms variational baseline methods on clips containing tool motions which occlude the points of interest, as is routinely observed in intra-operatively recorded surgery videos. CONCLUSIONS: The results indicate that complex synthetic training datasets can be used to specifically guide optical flow estimation. Our proposed algorithm therefore lays the foundation for a robust system which can assist with intra-operative tracking of moving surgical targets even when occluded.

Concentric Tube Robot Design and Optimization Based on Task and Anatomical Constraints.

Concentric tube robots are catheter-sized continuum robots that are well suited for minimally invasive surgery inside confined body cavities. These robots are constructed from sets of pre-curved superelastic tubes and are capable of assuming complex 3D curves. The family of 3D curves that the robot can assume depends on the number, curvatures, lengths and stiffnesses of the tubes in its tube set. The robot design problem involves solving for a tube set that will produce the family of curves necessary to perform a surgical procedure. At a minimum, these curves must enable the robot to smoothly extend into the body and to manipulate tools over the desired surgical workspace while respecting anatomical constraints. This paper introduces an optimization framework that utilizes procedureor patient-specific image-based anatomical models along with surgical workspace requirements to generate robot tube set designs. The algorithm searches for designs that minimize robot length and curvature and for which all paths required for the procedure consist of stable robot configurations. Two mechanics-based kinematic models are used. Initial designs are sought using a model assuming torsional rigidity. These designs are then refined using a torsionally-compliant model. The approach is illustrated with clinically relevant examples from neurosurgery and intracardiac surgery.

Biometry-based concentric tubes robot for vitreoretinal surgery.

Vitreoretinal surgery requires dexterous manoeuvres of tiny surgical tools in the confined cavity of the human eye through incisions made on the sclera. The fulcrum effect stemming from these incisions limits the safely reachable intraocular workspace and may result in scleral stress and collision with the intraocular lens. This paper proposes a concentric tube robot for panretinal interventions without risking scleral or lens damage. The robot is designed based on biometric measurements of the human eye, the required workspace, and the ease of incorporation in the clinical workflow. Our system is suited to 23 G vitreoretinal surgery, which does not require post-operative suturing, by comprising sub-millimetre concentric tubes. The proposed design is modular and features a rapid tube-exchange mechanism. To grasp and manipulate tissue, a sub-millimetre flexible gripper is fabricated. Experiments demonstrate the ability to reach peripheral retinal regions with limited motion at the incision point and no risk of lens contact.

Multi-view stereo and advanced navigation for transanal endoscopic microsurgery.

Transanal endoscopic microsurgery (TEM), i.e., the local excision of rectal carcinomas by way of a bimanual operating system with magnified binocular vision, is gaining acceptance in lieu of more radical total interventions. A major issue with this approach is the lack of information on submucosal anatomical structures. This paper presents an advanced navigation system, wherein the intraoperative 3D structure is stably estimated from multiple stereoscopic views. It is registered to a preoperatively acquired anatomical volume based on subject-specific priors. The endoscope motion is tracked based on the 3D scene and its field-of-view is visualised jointly with the preoperative information. Based on in vivo data, this paper demonstrates how the proposed navigation system provides intraoperative navigation for TEM1.

From passive tool holders to microsurgeons: safer, smaller, smarter surgical robots.

Within only a few decades from its initial introduction, the field of surgical robotics has evolved into a dynamic and rapidly growing research area with increasing clinical uptake worldwide. Initially introduced for stereotaxic neurosurgery, surgical robots are now involved in an increasing number of procedures, demonstrating their practical clinical potential while propelling further advances in surgical innovations. Emerging platforms are also able to perform complex interventions through only a single-entry incision, and navigate through natural anatomical pathways in a tethered or wireless fashion. New devices facilitate superhuman dexterity and enable the performance of surgical steps that are otherwise impossible. They also allow seamless integration of microimaging techniques at the cellular level, significantly expanding the capabilities of surgeons. This paper provides an overview of the significant achievements in surgical robotics and identifies the current trends and future research directions of the field in making surgical robots safer, smaller, and smarter.

Mobility experiments with microrobots for minimally invasive intraocular surgery.

PURPOSE: To investigate microrobots as an assistive tool for minimally invasive intraocular surgery and to demonstrate mobility and controllability inside the living rabbit eye. METHODS: A system for wireless magnetic control of untethered microrobots was developed. Mobility and controllability of a microrobot are examined in different media, specifically vitreous, balanced salt solution (BSS), and silicone oil. This is demonstrated through ex vivo and in vivo animal experiments. RESULTS: The developed electromagnetic system enables precise control of magnetic microrobots over a workspace that covers the posterior eye segment. The system allows for rotation and translation of the microrobot in different media (vitreous, BSS, silicone oil) inside the eye. CONCLUSIONS: Intravitreal introduction of untethered mobile microrobots can enable sutureless and precise ophthalmic procedures. Ex vivo and in vivo experiments demonstrate that microrobots can be manipulated inside the eye. Potential applications are targeted drug delivery for maculopathies such as AMD, intravenous deployment of anticoagulation agents for retinal vein occlusion (RVO), and mechanical applications, such as manipulation of epiretinal membrane peeling (ERM). The technology has the potential to reduce the invasiveness of ophthalmic surgery and assist in the treatment of a variety of ophthalmic diseases.