Subretinal injection is a critical procedure for delivering therapeutic agents to treat retinal diseases such as inherited retinal diseases (IRD) and age-related macular degeneration (AMD). However, retinal motion caused by physiological factors such as respiration and heartbeat significantly impacts precise needle positioning, increasing the risk of retinal pigment epithelium (RPE) damage. This paper presents a fully autonomous robotic subretinal injection system that integrates intraoperative optical coherence tomography (iOCT) imaging and deep learning-based motion prediction to synchronize needle and retinal motion. A Long Short-Term Memory (LSTM) neural network is used to predict internal limiting membrane (ILM) motion, outperforming a Fast Fourier Transform (FFT)-based baseline model. Additionally, a real-time registration framework aligns the needle tip position with the robot's coordinate frame. Then, a dynamic proportional speed control strategy ensures smooth and adaptive needle insertion. Experimental validation in both simulation and ex vivo open-sky porcine eyes demonstrates precise motion synchronization and successful subretinal injections. The experiments achieve a mean tracking error below 16.4 μm in pre-insertion phases. These results show the potential of AI-driven robotic assistance to improve the safety and accuracy of retinal microsurgery.

Exudative (wet) age-related macular degeneration (AMD) is a leading cause of vision loss in older adults, typically treated with intravitreal injections. Emerging therapies, such as subretinal injections of stem cells, gene therapy, small molecules and RPE cells require precise delivery to avoid damaging delicate retinal structures. Robotic systems can potentially offer the necessary precision for these procedures. This paper presents a novel approach for motion compensation in robotic subretinal injections, utilizing real time Optical Coherence Tomography (OCT). The proposed method leverages B5-scans, a rapid acquisition of small-volume OCT data, for dynamic tracking of retinal motion along the Z-axis, compensating for physiological movements such as breathing and heartbeat. Validation experiments on ex vivo porcine eyes revealed challenges in maintaining a consistent tool-to-retina distance, with deviations of up to 200 μm for 100 μm amplitude motions and over 80 μm for 25 μm amplitude motions over one minute. Subretinal injections faced additional difficulties, with phase shifts causing the needle to move off-target and inject into the vitreous. These results highlight the need for improved motion prediction and horizontal stability to enhance the accuracy and safety of robotic subretinal procedures.

Retinal vein cannulation (RVC) is a minimally invasive microsurgical procedure for treating retinal vein occlusion (RVO), a leading cause of vision impairment. However, the small size and fragility of retinal veins, coupled with the need for high-precision, tremor-free needle manipulation, create significant technical challenges. These limitations highlight the need for robotic assistance to improve accuracy and stability. This study presents an automated robotic system with a top-down microscope and B-scan optical coherence tomography (OCT) imaging for precise depth sensing. Deep learning-based models enable real-time needle navigation, contact detection, and vein puncture recognition, using a chicken embryo model as a surrogate for human retinal veins. The system autonomously detects needle position and puncture events with 85% accuracy. The experiments demonstrate notable reductions in navigation and puncture times compared to manual methods. Our results demonstrate the potential of integrating advanced imaging and deep learning to automate microsurgical tasks, providing a pathway for safer and more reliable RVC procedures with enhanced precision and reproducibility.

Modeling and controlling cable-driven snake robots is a challenging problem due to nonlinear mechanical properties such as hysteresis, variable stiffness, and unknown friction between the actuation cables and the robot body. This challenge is more significant for snake robots in ophthalmic surgery applications, such as the Improved Integrated Robotic Intraocular Snake (I^2RIS), given its small size and lack of embedded sensory feedback. Data-driven models take advantage of global function approximations, reducing complicated analytical models' challenge and computational costs. However, their performance might deteriorate in case of new data unseen in the training phase. Therefore, adding an adaptation mechanism might improve these models' performance during snake robots' interactions with unknown environments. In this work, we applied a model predictive path integral (MPPI) controller on a data-driven model of the I^2RIS based on the Gaussian mixture model (GMM) and Gaussian mixture regression (GMR). To analyze the performance of the MPPI in unseen robot-tissue interaction situations, unknown external disturbances and environmental loads are simulated and added to the GMM-GMR model. These uncertainties of the robot model are then identified online using a radial basis function (RBF) whose weights are updated using an extended Kalman filter (EKF). Simulation results demonstrated the robustness of the optimal control solutions of the MPPI algorithm and its computational superiority over a conventional model predictive control (MPC) algorithm.

Advancements in robot-assisted eye surgery have significantly enhanced precision in delicate procedures by enabling controlled movement of surgical instruments through a small entry point (trocar) placed on the outer layer of the eye (sclera). However, before these procedures can begin, the robot must first be prepared, draped, and calibrated. Then, it is positioned near the patient, where precise patient alignment is performed. Finally, the system ensures accurate alignment between the surgical instrument and the trocar, a critical step for safe and effective instrument insertion and surgical execution. Despite these advancements, integrating robotic systems into clinical practice remains challenging. The current manual preparation and alignment process is time-consuming and error-prone, emphasizing the need for a more automated and adaptive solution. Patient alignment time has been identified as a key factor differentiating robot-assisted and manual surgeries in overall procedure duration, with robot preparation and patient alignment extending surgery time by an average of 51 +/- 6 minutes compared to manual methods. To address these challenges, this paper introduces a multi-component robotic system combined with an image-guided patient alignment methodology designed to reduce preparation and alignment time in robotic eye surgery while maintaining surgical efficiency. The robotic system consists of a 2D-Cart carrying a 4-degree-of-freedom (DOF) frame robot holding a 5-DOF micro-manipulator. It utilizes an image-guided, multi-stage alignment approach, enabling the system to efficiently be localized and stabilized in an optimal position beside the surgical bed and ensuring precise and adaptive positioning relative to the patient in the surgical draped condition. The proposed end-to-end robotic system preparation and patient alignment are validated through a user study involving eight medical experts in a simulated surgical environment using a draped phantom eye. The results demonstrate successful robot-patient alignment in all cases, with the additional setup time significantly reduced to 272 +/- 84 seconds, highlighting the high precision of the system and clinical feasibility. To further assess the effectiveness of the image-guided alignment in real surgical settings, eye detection experiments are conducted on five patients under surgical draped conditions, achieving 90% eye detection accuracy and 75% iris detection accuracy. This study introduces a robust, semi-automated approach to robot-patient alignment, offering a more efficient and adaptable alternative to current manual methods by reducing setup time and enhancing surgical accuracy.INDEX TERMS Medical robots and systems, surgical robotics, planning, clinical integration in medical robotics.

Retinal surgery typically requires bimanual manipulation of tools in the eye. Freehand retinal vein cannulation (RVC) is a highly challenging operation mainly due to typical hand tremors relative to the small size of retinal veins. Robot-assisted technology resolves hand tremor issues and gives ophthalmologists higher positioning resolution to enable RVC. Bimanual robot manipulation of the eyeball typically requires kinematics-based control to maintain each robotic tools remote center of motion (RCM) constraint and registration between the two robots to avoid scleral injury. Any potential relative movement of the robot base can impact patient safety. To avoid these problems, we developed a bimanual adaptive cooperative (BMAC) control framework. Each robot is independently controlled via a hybrid adaptive position-force control algorithm using fiber Bragg grating-based force-sensing surgical instruments. This algorithm minimizes the tool-sclera interaction forces automatically, resulting in maintaining the sclera forces within a safe threshold and avoiding over-stretch of the sclera, which guarantees patient safety despite the absence of kinematic RCM constraint and registration of the two robots. The effectiveness of this approach is validated through a pilot study with five users in a vessel-following experiment on an eye phantom under a surgical microscope.

Retinal surgery requires extreme precision due to constrained anatomical spaces in the human retina. To assist surgeons achieve this level of accuracy, the Improved Integrated Robotic Intraocular Snake (I2RIS) with dexterous capability has been developed. However, such flexible tendon-driven robots often suffer from hysteresis problems, which significantly challenges precise control and positioning. In particular, we observed multi-stage hysteresis phenomena in the small-scale I2RIS. In this paper, we propose an Extended Generalized Prandtl-Ishlinskii (EGPI) model to increase the fitting accuracy of the hysteresis. The model incorporates a novel switching mechanism that enables it to describe multi-stage hysteresis in the regions of monotonic input. Experimental validation on I2RIS data demonstrates that the EGPI model outperforms the conventional Generalized Prandtl-Ishlinskii (GPI) model in terms of RMSE, NRMSE, and MAE across multiple motor input directions. The EGPI model in our study highlights the potential in modeling multi-stage hysteresis in minimally invasive flexible robots.

Robotic platforms provide consistent and precise tool positioning that significantly enhances retinal microsurgery. Integrating such systems with intraoperative optical coherence tomography (iOCT) enables image-guided robotic interventions, allowing autonomous performance of advanced treatments, such as injecting therapeutic agents into the subretinal space. However, tissue deformations due to tool-tissue interactions constitute a significant challenge in autonomous iOCT-guided robotic subretinal injections. Such interactions impact correct needle positioning and procedure outcomes. This paper presents a novel method for autonomous subretinal injection under iOCT guidance that considers tissue deformations during the insertion procedure. The technique is achieved through real-time segmentation and 3D reconstruction of the surgical scene from densely sampled iOCT B-scans, which we refer to as B5-scans. Using B5-scans we monitor the position of the instrument relative to a virtual target layer between the ILM and RPE. Our experiments on ex-vivo porcine eyes demonstrate dynamic adjustment of the insertion depth and overall improved accuracy in needle positioning compared to prior autonomous insertion approaches. Compared to a 35% success rate in subretinal bleb generation with previous approaches, our method reliably created subretinal blebs in 90% our experiments. The source code and data used in this study are publicly available on GitHub.

Recent advancements in age-related macular degeneration treatments necessitate precision delivery into the subretinal space, emphasizing minimally invasive procedures targeting the retinal pigment epithelium (RPE)-Bruch’s membrane complex without causing trauma. Even for skilled surgeons, the inherent hand tremors during manual surgery can jeopardize the safety of these critical interventions. This has fostered the evolution of robotic systems designed to prevent such tremors. These robots are enhanced by FBG sensors, which sense the small force interactions between the surgical instruments and retinal tissue. To enable the community to design algorithms taking advantage of such force feedback data, this paper focuses on the need to provide a specialized dataset, integrating optical coherence tomography (OCT) imaging together with the aforementioned force data. We introduce a unique dataset, integrating force sensing data synchronized with OCT B-scan images, derived from a sophisticated setup involving robotic assistance and OCT integrated microscopes. Furthermore, we present a neural network model for image-based force estimation to demonstrate the dataset’s applicability.

Retinal microsurgery is a high-precision surgery performed on a delicate tissue requiring the skill of highly trained surgeons. Given the restricted range of instrument motion in the confined intraocular space, snake-like robots may prove to be a promising technology to provide surgeons with greater flexibility, dexterity, and positioning accuracy during retinal procedures such as retinal vein cannulation and epireti-nal membrane peeling. Kinematics modeling of these robots is an essential step toward accurate position control. Unlike conventional manipulators, modeling these robots does not fol-low a straightforward method due to their complex mechanical structure and actuation mechanisms. The hysteresis problem can especially impact the positioning accuracy significantly in wire-driven snake-like robots. In this paper, we propose a data-driven kinematics model using a probabilistic Gaussian mixture model (GMM) and Gaussian mixture regression (GMR) approach with a hysteresis compensation algorithm. Experimental results on the two-degree-of-freedom (DOF) integrated robotic intraocular snake (1 2 RIS) show that the proposed model with the hysteresis compensation can predict the snake tip bending angle for pitch and yaw with 0.45° and 0.39° root mean square error (RMSE), respectively. This results in overall 60% and 70% improvements of accuracy for yaw and pitch over the same model without the hysteresis compensation.

Aim Routine alcohol testing of practicing physicians remains controversial since there are no uniform guidelines or legal regulations in the medical field. Our aim was to quantitatively study the acute and next-morning effects of breath alcohol concentration (BAC)-adjusted alcohol intake on overall simulated surgical performance and microtremor among senior vitreoretinal surgeons. Methods This prospective cohort study included 11 vitreoretinal surgeons (>10 years practice). Surgical performance was first assessed using the Eyesi surgical simulator following same-day alcohol consumption producing a BAC reading of 0.06%–0.10% (low-dose), followed by 0.11%–0.15% (high-dose). Dexterity was then evaluated after a ‘night out’ producing a high-dose BAC combined with a night’s sleep. Changes in the total score (0–700, worst-best) and tremor (0–100, best-worst) were measured. Results Surgeon performance declined after high-dose alcohol compared with low-dose alcohol (−8.60±10.77 vs −1.21±7.71, p=0.04, respectively). The performance during hangover was similar to low-dose alcohol (−1.76±14.47 vs −1.21±7.71, p=1.00, respectively). The performance during hangover tended to be better than after high-dose alcohol (−1.76±14.47 vs −8.60±10.77, p=0.09, respectively). Tremor increased during hangover compared with low-dose alcohol (7.33±21.65 vs −10.31±10.73, p=0.03, respectively). A trend toward greater tremor during hangover occurred compared with high-dose alcohol (7.33±21.65 vs −4.12±17.17, p=0.08, respectively). Conclusion Alcohol-related decline in simulated surgical dexterity among senior vitreoretinal surgeons was dose-dependent. Dexterity improved the following morning but remained comparable to after low-dose alcohol ingestion. Tremor increased during hangover compared with same-day intoxication. Further studies are needed to investigate extrapolations of these data to a real surgical environment regarding patient safety and surgeon performance.

Retinal surgery is a challenging procedure requiring precise manipulation of the fragile retinal tissue, often at the scale of tens-of-micrometers. Its difficulty has motivated the development of robotic assistance platforms to enable precise motion, and more recently, novel sensors such as microscope integrated optical coherence tomography (OCT) for RGB-D view of the surgical workspace. The combination of these devices opens new possibilities for robotic automation of tasks such as subretinal injection (SI), a procedure that involves precise needle insertion into the retina for targeted drug delivery. Motivated by this opportunity, we develop a framework for autonomous needle navigation during SI. We develop a system which enables the surgeon to specify waypoint goals in the microscope and OCT views, and the system autonomously navigates the needle to the desired subretinal space in real-time. Our system integrates OCT and microscope images with convolutional neural networks (CNNs) to automatically segment the surgical tool and retinal tissue boundaries, and model predictive control that generates optimal trajectories that respect kinematic constraints to ensure patient safety. We validate our system by demonstrating 30 successful SI trials on pig eyes. Preliminary comparisons to a human operator in robot-assisted mode highlight the enhanced safety and performance of our system.

Abstract The purpose of the study was to present a case of choroidal neovascularization (CNV) in unilateral acute idiopathic maculopathy (UAIM) associated with coxsackievirus B2. We present a case of UAIM with coxsackie B2 positive serology, presenting CNV as a complication. Follow-up multimodal retinal imaging was performed to characterize the macular abnormalities further. A 27-year-old woman complained of a sudden central visual field scotoma in her left eye (LE) 2 days before her presentation. The patient reported flu-like symptoms 2 weeks before her initial ophthalmic symptoms. Ophthalmologic examination and multimodal imaging were performed at the initial presentation and 2 months follow-up when CNV was detected. The patient underwent an intravitreous ranibizumab injection in the LE. After the injection, the optical coherence tomography was repeated and there was a partial decrement of the subretinal hyperreflective area, and visual acuity improved to 20/30. To the best of our knowledge, no previous reports were found linking coxsackie B2 infection with UAIM and CNV. The present case report demonstrates the importance of serial multimodal imaging to guarantee accurate diagnosis and effective therapy for patients with UAIM secondary to coxsackievirus infection, regardless of the virus variant involved.

PURPOSE:To evaluate novice and senior vitreoretinal surgeons after various exposures. Multiple comparisons ranked the importance of these exposures for surgical dexterity based on experience. METHODS:This prospective cohort study included 15 novice and 11 senior vitreoretinal surgeons (<2 and >10 years' practice, respectively). Eyesi-simulator tasks were performed after each exposure. Day 1, placebo, 2.5 mg/kg caffeine, and 5.0 mg/kg caffeine; day 2, placebo, 0.2 mg/kg propranolol, and 0.6 mg/kg propranolol; day 3, baseline simulation, breathalyzer readings of 0.06% to 0.10% and 0.11% to 0.15% blood alcohol concentrations; day 4, baseline simulation, push-up sets with 50% and 85% repetitions maximum; and day 5, 3-hour sleep deprivation. Eyesi-generated score (0-700, worst-best), out-of-tolerance tremor (0-100, best-worst), task completion time (minutes), and intraocular pathway (in millimeters) were measured. RESULTS:Novice surgeons performed worse after caffeine (-29.53, 95% confidence interval [CI]: -57.80 to -1.27, P = 0.041) and alcohol (-51.33, 95% CI: -80.49 to -22.16, P = 0.001) consumption. Alcohol caused longer intraocular instrument movement pathways (212.84 mm, 95% CI: 34.03-391.65 mm, P = 0.02) and greater tremor (7.72, 95% CI: 0.74-14.70, P = 0.003) among novices. Sleep deprivation negatively affected novice performance time (2.57 minutes, 95% CI: 1.09-4.05 minutes, P = 0.001) and tremor (8.62, 95% CI: 0.80-16.45, P = 0.03); however, their speed increased after propranolol (-1.43 minutes, 95% CI: -2.71 to -0.15 minutes, P = 0.029). Senior surgeons' scores deteriorated only following alcohol consumption (-47.36, 95% CI: -80.37 to -14.36, P = 0.005). CONCLUSION:Alcohol compromised all participants despite their expertise level. Experience negated the effects of caffeine, propranolol, exercise, and sleep deprivation on surgical skills.

A surgeon's physiological hand tremor can significantly impact the outcome of delicate and precise retinal surgery, such as retinal vein cannulation (RVC) and epiretinal membrane peeling. Robot-assisted eye surgery technology provides ophthalmologists with advanced capabilities such as hand tremor cancellation, hand motion scaling, and safety constraints that enable them to perform these otherwise challenging and high-risk surgeries with high precision and safety. Steady-Hand Eye Robot (SHER) with cooperative control mode can filter out surgeon's hand tremor, yet another important safety feature, that is, minimizing the contact force between the surgical instrument and sclera surface for avoiding tissue damage cannot be met in this control mode. Also, other capabilities, such as hand motion scaling and haptic feedback, require a teleoperation control framework. In this work, for the first time, we implemented a teleoperation control mode incorporated with an adaptive sclera force control algorithm using a PHANTOM Omni haptic device and a force-sensing surgical instrument equipped with Fiber Bragg Grating (FBG) sensors attached to the SHER 2.1 end-effector. This adaptive sclera force control algorithm allows the robot to dynamically minimize the tool-sclera contact force. Moreover, for the first time, we compared the performance of the proposed adaptive teleoperation mode with the cooperative mode by conducting a vessel-following experiment inside an eye phantom under a microscope.

Subretinal injection is an effective method for direct delivery of therapeutic agents to treat prevalent subretinal diseases. Among the challenges for surgeons are physiological hand tremor, difficulty resolving single-micron scale depth perception, and lack of tactile feedback. The recent introduction of intraoperative Optical Coherence Tomography (iOCT) enables precise depth information during subretinal surgery. However, even when relying on iOCT, achieving the required micron-scale precision remains a significant surgical challenge. This work presents a robot-assisted workflow for high-precision autonomous needle navigation for subretinal injection. The workflow includes online registration between robot and iOCT coordinates; tool-tip localization in iOCT coordinates using a Convolutional Neural Network (CNN); and tool-tip planning and tracking system using real-time Model Predictive Control (MPC). The proposed workflow is validated using a silicone eye phantom and ex vivo porcine eyes. The experimental results demonstrate that the mean error to reach the user-defined target and the mean procedure duration are within an acceptable precision range. The proposed workflow achieves a 100% success rate for subretinal injection, while maintaining scleral forces at the scleral insertion point below 15mN throughout the navigation procedures.

Retinal surgery is a complex medical procedure that requires high precision dexterity to perform delicate instrument maneuvers with sub-millimeter accuracy. Minimizing the manual tremor and achieving precise and repeatable execution of surgical tasks has motivated the development of robotic platforms to overcome the limitations of manual surgery. However, specific tasks, such as instrument insertion through the trocar, are more challenging in robotic surgery than in conventional manual procedures since the robot control is often optimized for navigation inside the eye. This challenges the integration of robotic systems, creating a high cognitive load on the operator and prolonging the surgery time. Moreover, misalignment of the robot's remote center of motion (RCM) and trocar position during the procedure can lead to excessive forces between the instrument and the trocar, potentially causing patient trauma. Precise and rapid localization of the trocars enables the automation of the insertion procedure and dynamic compensation of eye motion. In this work, we present a real-time marker-less method for 3D pose tracking of trocar, achieved with only a single monocular camera. Our experiments show promising results towards real-time trocar pose estimation and tracking, achieving an average error of 3 degrees in trocar orientation estimation, with an average processing time of 15 fps. This could serve as a foundation to improve robotic systems' automation, integration, and efficiency of robotic systems for retinal surgery. The dataset created for this work is made publicly available.

A potential Retinal Vein Occlusion (RVO) treatment involves Retinal Vein Cannulation (RVC), which requires the surgeon to insert a microneedle into the affected retinal vein and administer a clot-dissolving drug. This procedure presents significant challenges due to human physiological limitations, such as hand tremors, prolonged tool-holding periods, and constraints in depth perception using a microscope. This study proposes a robot-assisted workflow for RVC to overcome these limitations. The test robot is operated through a keyboard. An intraoperative Optical Coherence Tomography (iOCT) system is used to verify successful venous puncture before infusion. The workflow is validated using 12 ex vivo porcine eyes. These early results demonstrate a successful rate of 10 out of 12 cannulations (83.33%), affirming the feasibility of the proposed workflow.