A Data-Driven Model with Hysteresis Compensation for I2RIS Robot

Retinal microsurgery is a high-precision surgery performed on an exceedingly delicate tissue. It now requires extensively trained and highly skilled surgeons. Given the restricted range of instrument motion in the confined intraocular space, and also potentially restricting instrument contact with the sclera, snake-like robots may prove to be a promising technology to provide surgeons with greater flexibility, dexterity, space access, and positioning accuracy during retinal procedures requiring high precision and advantageous tooltip approach angles, such as retinal vein cannulation and epiretinal membrane peeling. Kinematics modeling of these robots is an essential step toward accurate position control, however, as opposed to conventional manipulators, modeling of these robots does not follow a straightforward method due to their complex mechanical structure and actuation mechanisms. Especially, in wire-driven snake-like robots, the hysteresis problem due to the wire tension condition can have a significant impact on the positioning accuracy of these robots. In this paper, we proposed an experimental kinematics model with a hysteresis compensation algorithm using the probabilistic Gaussian mixture models (GMM) Gaussian mixture regression (GMR) approach. Experimental results on the two-degree-of-freedom (DOF) integrated robotic intraocular snake (I2RIS) show that the proposed model provides 0.4 deg accuracy, which is an overall 60% and 70% of improvement for yaw and pitch degrees of freedom, respectively, compared to a previous model of this robot

Sonification as a reliable alternative to conventional visual surgical navigation

Despite the undeniable advantages of image-guided surgical assistance systems in terms of accuracy, such systems have not yet fully met surgeons' needs or expectations regarding usability, time efficiency, and their integration into the surgical workflow. On the other hand, perceptual studies have shown that presenting independent but causally correlated information via multimodal feedback involving different sensory modalities can improve task performance. This article investigates an alternative method for computer-assisted surgical navigation, introduces a novel four-DOF sonification methodology for navigated pedicle screw placement, and discusses advanced solutions based on multisensory feedback. The proposed method comprises a novel four-DOF sonification solution for alignment tasks in four degrees of freedom based on frequency modulation synthesis. We compared the resulting accuracy and execution time of the proposed sonification method with visual navigation, which is currently considered the state of the art. We conducted a phantom study in which 17 surgeons executed the pedicle screw placement task in the lumbar spine, guided by either the proposed sonification-based or the traditional visual navigation method. The results demonstrated that the proposed method is as accurate as the state of the art while decreasing the surgeon's need to focus on visual navigation displays instead of the natural focus on surgical tools and targeted anatomy during task execution

Robotic Navigation Autonomy for Subretinal Injection via Intelligent Real-Time Virtual iOCT Volume Slicing

In the last decade, various robotic platforms have been introduced that could support delicate retinal surgeries. Concurrently, to provide semantic understanding of the surgical area, recent advances have enabled microscope-integrated intraoperative Optical Coherent Tomography (iOCT) with high-resolution 3D imaging at near video rate. The combination of robotics and semantic understanding enables task autonomy in robotic retinal surgery, such as for subretinal injection. This procedure requires precise needle insertion for best treatment outcomes. However, merging robotic systems with iOCT introduces new challenges. These include, but are not limited to high demands on data processing rates and dynamic registration of these systems during the procedure. In this work, we propose a framework for autonomous robotic navigation for subretinal injection, based on intelligent real-time processing of iOCT volumes. Our method consists of an instrument pose estimation method, an online registration between the robotic and the iOCT system, and trajectory planning tailored for navigation to an injection target. We also introduce intelligent virtual B-scans, a volume slicing approach for rapid instrument pose estimation, which is enabled by Convolutional Neural Networks (CNNs). Our experiments on ex-vivo porcine eyes demonstrate the precision and repeatability of the method. Finally, we discuss identified challenges in this work and suggest potential solutions to further the development of such systems

Remote Interactive Surgery Platform (RISP): Proof of Concept for an Augmented-Reality-Based Platform for Surgical Telementoring

The "Remote Interactive Surgery Platform" (RISP) is an augmented reality (AR)-based platform for surgical telementoring. It builds upon recent advances of mixed reality head-mounted displays (MR-HMD) and associated immersive visualization technologies to assist the surgeon during an operation. It enables an interactive, real-time collaboration with a remote consultant by sharing the operating surgeon's field of view through the Microsoft (MS) HoloLens2 (HL2). Development of the RISP started during the Medical Augmented Reality Summer School 2021 and is currently still ongoing. It currently includes features such as three-dimensional annotations, bidirectional voice communication and interactive windows to display radiographs within the sterile field. This manuscript provides an overview of the RISP and preliminary results regarding its annotation accuracy and user experience measured with ten participants

BFS-Net: Weakly Supervised Cell Instance Segmentation from Bright-Field Microscopy Z-Stacks

Despite its broad availability, volumetric information acquisition from Bright-Field Microscopy (BFM) is inherently difficult due to the projective nature of the acquisition process. We investigate the prediction of 3D cell instances from a set of BFM Z-Stack images. We propose a novel two-stage weakly supervised method for volumetric instance segmentation of cells which only requires approximate cell centroids annotation. Created pseudo-labels are thereby refined with a novel refinement loss with Z-stack guidance. The evaluations show that our approach can generalize not only to BFM Z-Stack data, but to other 3D cell imaging modalities. A comparison of our pipeline against fully supervised methods indicates that the significant gain in reduced data collection and labelling results in minor performance difference

A Tool for High-Resolution Volumetric Optical Coherence Tomography by Compounding Radial-and Linear Acquired B-Scans Using Registration

Optical coherence tomography (OCT) is a medical imaging modality that is commonly used to diagnose retinal diseases. In recent years, linear and radial scanning patterns have been proposed to acquire three-dimensional OCT data. These patterns show differences in A-scan acquisition density across the generated volumes, and thus differ in their suitability for the diagnosis of retinal diseases. While radial OCT volumes exhibit a higher A-scan sampling rate around the scan center, linear scans contain more information in the peripheral scan areas. In this paper, we propose a method to combine a linearly and radially acquired OCT volume to generate a single compound volume, which merges the advantages of both scanning patterns to increase the information that can be gained from the three-dimensional OCT data. We initially generate 3D point clouds of the linearly and radially acquired OCT volumes and use an Iterative Closest Point (ICP) variant to register both volumes. After registration, the compound volume is created by selectively exploiting linear and radial scanning data, depending on the A-scan density of the individual scans. Fusing regions from both volumes with respect to their local A-scan sampling density, we achieve improved overall anatomical OCT information in a high-resolution compound volume. We demonstrate our method on linear and radial OCT volumes for the visualization and analysis of macular holes and the surrounding anatomical structures

Ticagrelor in patients with diabetes and stable coronary artery disease with a history of previous percutaneous coronary intervention (THEMIS-PCI) : a phase 3, placebo-controlled, randomised trial

Background: Patients with stable coronary artery disease and diabetes with previous percutaneous coronary intervention (PCI), particularly those with previous stenting, are at high risk of ischaemic events. These patients are generally treated with aspirin. In this trial, we aimed to investigate if these patients would benefit from treatment with aspirin plus ticagrelor. Methods: The Effect of Ticagrelor on Health Outcomes in diabEtes Mellitus patients Intervention Study (THEMIS) was a phase 3 randomised, double-blinded, placebo-controlled trial, done in 1315 sites in 42 countries. Patients were eligible if 50 years or older, with type 2 diabetes, receiving anti-hyperglycaemic drugs for at least 6 months, with stable coronary artery disease, and one of three other mutually non-exclusive criteria: a history of previous PCI or of coronary artery bypass grafting, or documentation of angiographic stenosis of 50% or more in at least one coronary artery. Eligible patients were randomly assigned (1:1) to either ticagrelor or placebo, by use of an interactive voice-response or web-response system. The THEMIS-PCI trial comprised a prespecified subgroup of patients with previous PCI. The primary efficacy outcome was a composite of cardiovascular death, myocardial infarction, or stroke (measured in the intention-to-treat population). Findings: Between Feb 17, 2014, and May 24, 2016, 11 154 patients (58% of the overall THEMIS trial) with a history of previous PCI were enrolled in the THEMIS-PCI trial. Median follow-up was 3·3 years (IQR 2·8–3·8). In the previous PCI group, fewer patients receiving ticagrelor had a primary efficacy outcome event than in the placebo group (404 [7·3%] of 5558 vs 480 [8·6%] of 5596; HR 0·85 [95% CI 0·74–0·97], p=0·013). The same effect was not observed in patients without PCI (p=0·76, p interaction=0·16). The proportion of patients with cardiovascular death was similar in both treatment groups (174 [3·1%] with ticagrelor vs 183 (3·3%) with placebo; HR 0·96 [95% CI 0·78–1·18], p=0·68), as well as all-cause death (282 [5·1%] vs 323 [5·8%]; 0·88 [0·75–1·03], p=0·11). TIMI major bleeding occurred in 111 (2·0%) of 5536 patients receiving ticagrelor and 62 (1·1%) of 5564 patients receiving placebo (HR 2·03 [95% CI 1·48–2·76], p<0·0001), and fatal bleeding in 6 (0·1%) of 5536 patients with ticagrelor and 6 (0·1%) of 5564 with placebo (1·13 [0·36–3·50], p=0·83). Intracranial haemorrhage occurred in 33 (0·6%) and 31 (0·6%) patients (1·21 [0·74–1·97], p=0·45). Ticagrelor improved net clinical benefit: 519/5558 (9·3%) versus 617/5596 (11·0%), HR=0·85, 95% CI 0·75–0·95, p=0·005, in contrast to patients without PCI where it did not, p interaction=0·012. Benefit was present irrespective of time from most recent PCI. Interpretation: In patients with diabetes, stable coronary artery disease, and previous PCI, ticagrelor added to aspirin reduced cardiovascular death, myocardial infarction, and stroke, although with increased major bleeding. In that large, easily identified population, ticagrelor provided a favourable net clinical benefit (more than in patients without history of PCI). This effect shows that long-term therapy with ticagrelor in addition to aspirin should be considered in patients with diabetes and a history of PCI who have tolerated antiplatelet therapy, have high ischaemic risk, and low bleeding risk