Evaluating Force and Motion in Posterior Vitreous Detachment Manoeuvres Using a Robotic Data Acquisition System in Cadaveric Human Eyes

Evaluating Force and Motion in Posterior Vitreous Detachment Manoeuvres Using a Robotic Data Acquisition System in Cadaveric Human Eyes | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Evaluating Force and Motion in Posterior Vitreous Detachment Manoeuvres Using a Robotic Data Acquisition System in Cadaveric Human Eyes Reza Heidari, Esmaeil Asadi Khameneh, Ali Rastaghi, Mohammad Reza Dindarloo, and 9 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6634699/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 12 Dec, 2025 Read the published version in Scientific Reports → Version 1 posted 16 You are reading this latest preprint version Abstract Deep vitrectomy, a complex ophthalmic procedure, requires significant surgical skill. This study evaluated the ARASH:ASiST robotic system for real-time, quantifiable assessment of deep vitrectomy in a pre-clinical setting using cadaveric human eyes. Four surgeons, with varying experience levels, performed procedures while intraoperative force, positional, and temporal data were recorded. Analysis of seven surgical datasets revealed experience-specific differences in force application, particularly during posterior vitreous detachment induction, which emerged as a key performance metric. Time data provided insights into surgeon control. The system's real-time graphical user interface offered immediate feedback and facilitated postoperative analysis. Usability assessments confirmed the system's practicality and non-intrusiveness. The ARASH:ASiST system demonstrates potential as an objective platform for evaluating and understanding surgical skill in deep vitrectomy, offering a novel approach to surgical training and assessment. Physical sciences/Engineering Health sciences/Medical research/Translational research Vitreoretinal Surgery Surgical Data Acquisition Cadaveric Human Eyes Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Introduction Modern surgical data acquisition (DAQ) systems enable precise recording and analysis of surgical techniques through multimodal sensing technologies 1 . Among these, force and positional measurements provide critical intuition into technical proficiency through the high-precision capture of key surgical metrics, such as instrument trajectories 2 and hand forces 3 . Historically, operating room (OR) data collection depended on subjective observations and debriefing checklists, which were often 4 prone to biases and lacked scalability 5 . Despite advancements in DAQ systems, many existing solutions disrupt surgical workflows and fail to preserve the intricate precision of ophthalmic techniques. This highlights the need for non-intrusive approaches that align seamlessly with the demands of microsurgical procedures. To address this challenge, we present ARASH:ASiST, a transparent robotic platform specifically designed for VR microsurgery. While primarily enhancing surgical precision and control through tactile exchange during vitrectomy learning and teaching, it can also be used for seamless and authentic intraoperative data collection for objective skills assessment. Through pre-clinical validation, ARASH:ASiST demonstrated its ability to capture delicate surgical movements and provide objective analysis across various stages of vitrectomy procedure. Recent advancements in AI-driven surgical skill assessment have demonstrated the potential for automated and objective evaluation of surgeon expertise through robotic data acquisition, reducing the subjectivity inherent in traditional assessment methods 4 . The ARASH:ASiST system aligns with these developments by providing force and trajectory-based performance metrics that can be used for skill evaluation and training. Its Graphical User Interface (GUI) further supports detailed visualization of surgeons' movements, facilitating comparisons of skill levels among practitioners. This system not only advances our understanding of surgical techniques and psychomotor demands but also represents a pivotal step toward developing comprehensive databases of best practices, ultimately enhancing precision, reducing errors, and improving surgical outcomes. Materials and Methods Tissue Preparation We used postmortem human cadaveric eyes, which has been stored in a lab fridge at -20°C in the Eye Bank of Farabi Eye Hospital (Tehran, Iran). Globes were obtained from a 23-year-old Iranian male cadaver, which tested positive for Human Immunodeficiency Virus, ineligible for corneal transplantation and used in the mechanical test session, and a 47-year-old female cadaver, positive for Hepatitis C Virus, which was engaged in the DAQ session. The tissues were maintained in a sealed iced package the day prior to the experiment (Fig. 1 .b). Tissues were brought up to room temperature, in order to prevent tissue from temperature shock prior to placing in the holder. Ethical Approval The study protocol was approved by the Research Ethics Committee of Tehran University of Medical Sciences (IR.TUMS.FARABIH.REC.1395.4) and National Institute for Medical Research Development (IR.NIMAD.REC.1395.032). Vitrectomy The cadaveric eye was placed on an eye holder fixed to a mannequin head and held in place by the negative pressure of the eye holder 6 . The cadaveric eye and the eye holder were then positioned in the right orbit of the mannequin head. The surgeon sat superior to the mannequin head after draping field, imitating a sterile condition. Three ports of 23-gauge valved trocars were placed 3.5 mm off the limbus: one in the inferotemporal quadrant for the irrigation cannula, one in the superotemporal quadrant, and one in the superonasal quadrant for the light port and vitrectomy probe. The irrigation pressure was set to 30 cm of water height to maintain the intraocular pressure. An anterior segment surgeon [SFM] removed the cadaver cornea using an 8 mm vacuum trephine. The crystalline lens was also removed open-sky and delicately to enhance the view of the posterior segment. A temporary PMMA keratoprosthesis (KPro) with trunk was then sutured to the bed with interrupted 8-0 Vicryl sutures. Visualization of the posterior segment was provided by the Haag-Streit surgical microscope enhanced with a 90-diopter BIOM® lens (HS Hi-R NEO 900A, Haag-Streit, Wedel, Germany) and the Haag-Streit C.MOR HD surgical microscope camera and recording system. The experienced retina surgeon [HR] performed the deep vitrectomy act from the other two trocar ports using the DORC EVA vitrectomy machine (vitreous cutter; Dutch Ophthalmic Research Centre, Zuidland, Netherlands). The vitrectomy probe was set with a cut rate of 3500 cuts per minute and a vacuum of 300 mmHg. It was attached to the haptic device, while held and directly operated by the surgeon. The retina surgeon acted core vitrectomy in all quadrants of the vitreous cavity. Core vitrectomy is a surgical procedure performed to remove the core vitreous gel from the central part of the posterior segment of the eye in all quadrants. As mentioned, the cadaveric eye had been held frozen for more than one year and we found total retinal detachment. The detached peripheral retina was removed first with the vitrectome probe to facilitate performing peripheral vitreous shaving. To carry out these tasks, the surgeon [HR] moved the probe circumferentially, covering the entire peripheral retina, while also rotating the eye to visualize the periphery, ensuring adequate exposure of the farthest internal periphery of the cadaveric eye. The feedback from the surgeon regarding the ease of these manoeuvres was recorded. To assess the ease of handling the vitrectomy probe during the procedure and peripheral vitreous shaving on the right socket of the mannequin head, two different retina surgeons, one right-handed [HR] and one left-handed [EA], performed the procedures. The vitrectomy probe was mounted with the robot, allowing movement in all possible directions during vitrectomy. The objective was to record and compare the ease of handling the probe for both surgeons, considering their dominant hand preferences. Haptic System The proposed haptic system, ARASH:ASiST, has transferred intraoperative surgical data collection and analysis. Through its vitrectomy probe-like interface, precise positional and force data from surgeons are captured in the OR—a capability previously unattainable without significantly disrupting the natural workflow of VR surgery. In vitrectomy and other minimally invasive surgery (MIS) procedures, instruments access the target organ through small incisions. The endoilluminator and primary instruments, such as the vitrector, are inserted through trocar ports. The haptic system’s end effector, which houses the vitrector, provides three rotational and one translational degree of freedom (DoF). These DoFs intersect at the insertion port to create a remote centre of motion (RCM), generating a spherical sector workspace. Mechanically engineered via a parallelogram mechanism, this RCM configuration simplifies kinematics, enables near-decoupled dynamics, and enhances structural rigidity and manoeuvrability. Consequently, surgeons can manipulate the instrument with improved ease and precision. In the design of ARASH:ASiST for minimally invasive ophthalmic surgery, several critical features optimize performance, including low inertia, zero backlash, and full back-drivability achieved through a capstan drive system. Lightweight aluminium links, strategic mass balancing, and compact cable-pulley mechanisms enhance precision, agility, and reliability. Ergonomic considerations minimize surgeon fatigue, while easy maintenance and RCM calibration ensure consistent performance and safety. To optimize the surgical field and positioning, the system features an anatomically accurate eye holder for realistic tissue manipulation and a custom 3-DoF table for seamless integration with perioperative equipment. This setup ensures precise alignment of the robot's RCM with the sclerotomy port, enabling accurate and stable instrument entry into the globe. The ARASH:ASiST system adopts an innovative approach by embedding data acquisition hardware directly into the robot structure, eschewing conventional commercial peripheral component interconnect (PCI) cards. This strategy offers cost-effectiveness and a unified hardware framework throughout development and production. An Ethernet interface facilitates user datagram protocol (UDP) communication between the embedded controller and target computer. The system's core comprises an ARM CORTEX-M7 microcontroller, serving as a data bridge and executing computationally intensive controllers and observers. The 1 kHz sampling rate of the communication link suffices for the robot's rapid transient responses. Control commands from the target computer are routed to a serial peripheral interface (SPI) digital to analogue converter (DAC) chip, generating analogue torque commands for the motor driver in current control mode. Simultaneously, the microcontroller interprets motor encoder signals and relays them to the target computer, establishing a closed-loop control system. Graphical User Interface (GUI) A GUI was developed for the ARASH:ASiST and implemented during the DAQ session 7 . The interface features a schematic eye model, created using computer-aided design (CAD) software based on average anatomical parameters 8 . This model serves as an interactive component within the GUI, with the surgical trajectory displayed relative to the eye model to provide a realistic, real-time visualization. Additionally, the GUI visualizes applied force by translating measurements from the haptic system into color-coded trajectories. To achieve precise calibration of the eye model to match the dimensions of the cadaveric eye, an expert surgeon accessed the optic nerve head while kinematic data from the haptic system was calculated in real time. This data was used to determine the exact distance between the trocar port and optic nerve head, which was then set in the GUI. After calibration, the minimal remaining error was solely due to forward kinematic approximations and subtle variations in the surgeon’s skill, ensuring that the 3D model remained aligned with the anatomical dimensions throughout the session. Data Acquisition (DAQ) Protocol Trajectory-based analysis has been recognized as a key method for evaluating surgical proficiency, as it enables the identification of skill-related features such as smoothness, efficiency, and motion economy 9 . The ARASH:ASiST system builds upon this approach by capturing real-time force and positional data to quantify surgeon expertise. A detailed DAQ instruction guide was prepared, offering an overview of previous clinical tests, integrating relevant feedback, and outlining the objectives of the DAQ session. The guide was shared with surgeons and attendees prior to the event. During the session, data was systematically recorded by each surgeon across ten subphases of deep vitrectomy, categorized into three procedural stages: Entry and Initial Handling , Core Vitrectomy , and PVD Induction . The subphases included: Entry, Local vitrectomy (near trocar ports), Anterior Core (removal of vitreous posterior to the pupil), Inferonasal, Inferotemporal, Superonasal, Superotemporal quadrant core vitrectomy, PVD induction, and Exit . During the PVD induction, surgeons employed the drunk walk (DW) technique 10 . This method involves placing the probe near the retinal surface adjacent to the optic disc and applying vacuum suction to engage the vitreous body. The probe is then advanced in a controlled zigzag motion along the expected path of the inferotemporal vascular arcade and anteriorly, facilitating detachment of the posterior vitreous from the retina. The technique was specifically adapted to address the total retinal detachment in the cadaveric eye, with manoeuvres guided by anatomical landmarks and expected structural patterns. Throughout the operation, the vitrectomy probe was positioned with the cutter facing upward to ensure visibility of the cutting portion. Results During an initial pre-clinical test, we evaluated the haptic design and mechanical performance of the system (Fig. 1), confirming its effectiveness in resolving surgeon-reported issues. Building on this, a DAQ session was conducted during vitrectomy acts (Fig. 2). To ensure structured data collection, the DAQ protocol guided attendees through the procedures in the OR at Farabi Hospital. System Integration and Workflow Compatibility To assess the ARASH:ASiST system’s capacity to integrate seamlessly into the OR without disrupting natural surgical workflows, eight domain-specific features were evaluated: 1) integration within OR environment (workflow synchronization), 2) sterilization (adherence to aseptic protocols), 3) OR equipment positioning (compatibility with standard layouts), 4) eye holder (anatomic fixation fidelity), 5) mannequin head (procedural authenticity), 6) robotic system interface (instrument control responsiveness), 7) robot table (positioning flexibility), and 8) GUI (real-time data visualization). A cohort of 14 preclinical users—comprising two VR fellows, two intermediate VR surgeons, two experienced VR surgeons, two cornea surgeons, three research assistants, and three OR technicians—completed a postoperative questionnaire. The questionnaire utilized a 5-point Likert scale to assess the level of comfort across multiple aspects of the surgical setup and procedure, ranging from 1 (Extremely Uncomfortable) to 5 (Extremely Comfortable), as shown in Fig. 3. Feedback was stratified by role to ensure clinical relevance: VR surgeons evaluated all eight features, while cornea surgeons assessed five features (excluding robotic interface, robot table, and GUI). Support staff (assistants/technicians and researchers) focused on three domains critical to workflow efficiency: sterilization, OR equipment positioning, and integration within the OR environment. Vitrectomy Data The dataset comprises seven surgical recordings under standardized conditions, including contributions from two fellows (4 years), and one expert (>8 years). The intermediate surgeon acted four procedures: two on the mannequin’s right eye (right-handed) and two on the left eye (left-handed). All utilized the ARASH:ASiST system, with instrument kinematics (Encoder1/2 in degrees, Linear Encoder3 in mm) and forces (Newtons) recorded at 1 kHz following initial calibration. During the operation, the eye is often tilted to provide better visualization and access to specific areas of the posterior segment. The collected data reveals that in certain phases, specifically the DW method, where access to the optic nerve is required, this tilting is particularly pronounced. Notably, in the data from the left-handed surgeon, the eye is slightly tilted to enhance access and visibility, as shown in Fig. 4.c. The DW method, as the name suggests, mimics the staggered movement characteristic of a person walking under the influence of alcohol. This distinctive trajectory is evident in the 3D plots of the surgeon's movements, where it appears as a unique pattern, as shown in Fig. 4.b and Fig. 4.d. The recorded forces in the haptic system ranged from -3 N to +6 N across the entire operation and all recorded data. The sign of the force reflects opposing movements within the haptic system. This range varied across different subphases of the procedure. For example, during the DW Method, the force data patterns enabled the differentiation of surgeons, based on their dominant hand. The force range for right-dominant surgeons spanned from -0.5 N to 5.89 N, while the left-dominant surgeon exhibited a narrower range from -2.96 N to -0.7 N. These differences are also evident in the trajectory visualizations, where positive forces are represented by yellow to red hues and negative forces by shades of purple. The trajectory of the left-dominant surgeon remained entirely within the purple spectrum, whereas right-dominant surgeons displayed trajectories in yellow to red, as shown in Fig. 4. Hand-dominance has been found to influence robotic-assisted surgical performance, with measurable differences in trajectory control and force application between right-handed and left-handed surgeons 11 . Our findings confirm this effect, as demonstrated by the distinct force patterns observed during posterior vitreous detachment induction. The two surgical fellows in the study were right-dominant and consistently demonstrated significantly higher force values during the DW subphase of the operation. This contributed to a broader force range observed across all surgical data, with the positive limit extending to 5.2 N and the negative limit confined to -2 N, as shown in Fig. 5. The elevated positive force values reflect the fellows' techniques, in contrast to the intermediate left-dominant surgeon, whose lower force values resulted in a narrower negative range. Additionally, when comparing the force ranges of the expert surgeon during this phase, it becomes evident that the fellows' less advanced techniques led to the application of forces that were substantially higher than necessary for a precise and controlled operation. This highlights how variations in expertise and technique influence force application during specific surgical phases. The ground truth in this analysis is based on the level of experience of each surgeon. Discussion In this study, all seven surgical act datasets were recorded on the same cadaveric eye. The initial procedure presented challenges due to the presence of vitreous and retinal detachment, which impacted the surgical environment. However, the removal of the vitreous during the first procedure established consistent conditions for all subsequent recordings. The objective was to systematically navigate the posterior segment of the eye, phase by phase, in accordance with the DAQ protocol. Despite variations in individual surgical techniques and preferences, certain subphases, such as the DW method, follow predefined trajectories that enable comparative analysis across surgeons. This consistency provides a foundation for extracting novel insights from the recorded force and positional data, which can be used to analyse specific aspects of surgical performance. These findings underscore the potential of the collected data to deepen our understanding of vitrectomy procedures, offering valuable perspectives on both the operation itself and the application of haptics and robotics in VR surgery, its training and assessment. Combining domain knowledge-based metrics with machine learning techniques has been shown to improve the accuracy and interpretability of surgical skill assessments 12 . Our study leverages this hybrid approach by integrating real-time force and trajectory measurements with expert evaluations to create a more comprehensive skill assessment framework. The duration of each phase is a crucial factor in evaluating surgical performance. However, a shorter phase duration does not necessarily indicate superior performance, as precision and thoroughness are essential for achieving optimal outcomes. Interestingly, in some subphases, the fellows exhibit shorter phase durations compared to the intermediate and expert surgeons. However, for the DW method, the time to completion is approximately 27 seconds for the expert surgeon, 36 seconds for the intermediate surgeon, and 59 seconds for the fellows. This trend highlights the balance between time and technique in this particular subphase, emphasizing that while efficiency is valuable, the approach of experienced surgeons ensures both accuracy and control while completing the task within a reasonable time frame. The time spent in the DW method procedure is divided into three stages. The Approaching Stage, which involves reaching the optic nerve; the Operational Stage, which includes the DW method procedure itself; and the Receding Stage, which represents the final movement of receding from the main operating region. These stages are illustrated in Fig. 6. The DORC EVA machine, an advanced aspiration system enabling both flow and vacuum control modes, was employed during data collection10. This platform is designed to enhance intraoperative fluidic stability, allowing for precise control of intraocular pressure, which was critical in maintaining the stability of the cadaveric eye. The flow control capability of the DORC EVA machine facilitated consistent pressure regulation, tailored to the experimental requirements, enabling the successful acquisition of seven surgical data from a single cadaveric eye. However, minor variations in the flow dynamics across datasets led to subtle changes in the eyeball's shape and dimensions over time. These slight alterations introduced a small range of variability in the data. Given the accurate calibration of the haptic system, the observed differences are attributed to these minor variations in the intraocular fluid dynamics rather than the instrumentation inconsistencies. This underscores the inherent sensitivity of cadaveric tissue to procedural conditions, even in highly controlled settings. GUI Feedback The GUI provided real-time feedback, serving as a valuable tool for the observing surgeons’ performance. While the operating surgeon remained focused on the procedure through the microscope and did not directly utilize the GUI during the surgery, it proved highly beneficial for the observers. The GUI offered clear visual feedback cues, effectively indicating the current stage of the operation (Fig. 4). The observing surgeons were able to monitor the operating surgeon's approach to each section of the posterior segment, including specific techniques such as PVD induction. This facilitated enhanced communication and skill transfer, allowing for collaborative guidance during the procedure. Surgeons could discuss and refine techniques in real time, fostering an interactive learning environment. Furthermore, the GUI data could be reviewed postoperatively, offering valuable insights for analysis and skill development. In the standard feedback framework, the observation is through accessory teaching oculars or the online video screen which streams the surgical procedure. Limitations and Future Perspective This study, while providing foundational insights into robotic-assisted VR surgical data acquisition, is subject to an inherent limitation due to its preclinical nature. The absence of an intact vitreous body in cadaveric eyes precluded evaluation of surgical steps efficiency under real-world clinical conditions, and postmortem retinal detachment necessitated removal of the peripheral retina, altering the procedural context compared to a real surgery. Additionally, there is no universal deep vitrectomy manoeuvres set, as steps were surgeon-dependent; not all phases of a complete vitrectomy were performed either. The small cohort size further limited normative data collection across experience levels and handedness, underscoring the need for broader validation, for instance by including mid- to late carrier VR surgeons. Technically-speaking, we collected the axial and lateral driving force, positional/trajectory, and temporal data of simulated deep vitrectomy procedure by the ARASH:ASiST system. In real vitrectomy surgery there is another key element, i.e. rotational dynamism of the vitrectomy probe. ARASH:ASiST is not able to register this aspect which constitutes an integral element of surgical efficiency and adroitness. Future efforts will prioritize expanding the dataset to address these constraints. Physiologically realistic ocular phantoms with synthetic vitreous can be employed to replicate in vivo conditions. Procedural variability can be minimized through pre-trial discussion and agreements. In a multi-institutional collaboration we can recruit a larger cohort of surgeons stratified by experience (naïve to expert) and handedness, ensuring robust normative benchmarks. Synchronization of the ARASH:ASiST kinematic and force data with vitrectomy machine parameters (cut rate, vacuum, etc.) and intraoperative imaging will enable multimodal analytics, refining skill assessment frameworks. This enriched dataset will underpin AI-driven performance evaluations, generating surgeon-specific feedback reports to personalize training. It is note-worthy that the ARASH:ASiST is basically a dual haptic system for real-time haptic exchange. Our current report proves the concept that surgical education can be further enhanced, not only by haptic feedback but also by a robotic data acquisition system. These will finally, improve patient safety and microsurgical training efficiency. Abbreviations Abbreviation Description DAQ Data Acquisition OR Operating Room ARASH:ASiST ARAS HAptic for EYE Surgery Training GUI Graphical User Interface KPro Keratoprosthesis MIS Minimally Invasive Surgery DoF Degree of Freedom RCM Remote Centre of Motion WC Weight Compensation PCI Peripheral Component Interconnect UDP User Datagram Protocol SPI Serial Peripheral Interface DAC Digital to Analogue Converter CAD Computer-Aided Design PVD Posterior Vitreous Detachment DW Drunk Walk VR Vitreoretinal Declarations Financial Support: This work partially funded by the Iran National Science Foundation (INSF) (No.4024081), and The National Centre for Strategic Research in Medical Education (NASR) (No. 4010324). The sponsors or funding organizations had no role in the design or conduct of this research. Conflict of Interest: No conflicting relationship exists for any author. Data availability The datasets used and/or analysed during the current study available from the corresponding author on reasonable request. References Levin, M. et al. Surgical data recording in the operating room: a systematic review of modalities and metrics. British Journal of Surgery 108 , 613–621 (2021). Hubschman, J. P., Son, J., Allen, B., Schwartz, S. D. & Bourges, J. L. Evaluation of the motion of surgical instruments during intraocular surgery. Eye 25 , 947–953 (2011). Teixeira, A. et al. Vitreoretinal traction created by conventional cutters during vitrectomy. Ophthalmology 117 , 1387–1392 (2010). Soleymani, A., Li, X. & Tavakoli, M. Artificial intelligence in robot-assisted surgery: Applications to surgical skills assessment and transfer. Medical and Healthcare Robotics 183–200 (2023). McQueen, S. A. et al. Examining the barriers to meaningful assessment and feedback in medical training. The American Journal of Surgery 211 , 464–475 (2016). Mohammadi, S. F., Mazouri, A., Rahman-A, N., Jabbarvand, M. & Peyman, G. A. Globe-fixation system for animal eye practice. J Cataract Refract Surg 37 , 4–7 (2011). Aghapour, M., Heidari, R., Nazeri, M. M., Ahmadi, M. J. & Taghirad, H. D. Development and Integration of an Advanced GUI with Real-Time Data Logging for RCM-Based Robotic Surgery Devices. ICRoM 2024 - 12th RSI International Conference on Robotics and Mechatronics 426–430 (2024) doi:10.1109/ICROM64545.2024.10903536. Yang, Y. et al. Safety control method of robot-assisted cataract surgery with virtual fixture and virtual force feedback. J Intell Robot Syst 97 , 17–32 (2020). Soleymani, A., Li, X. & Tavakoli, M. Surgical procedure understanding, evaluation, and interpretation: A dictionary factorization approach. IEEE Trans Med Robot Bionics 4 , 423–435 (2022). Storey, P. P. & Garg, S. J. The Drunk Walk Method for Posterior Vitreous Detachment Induction. American Academy of Ophthalmology (2019). Soleymani, A. et al. Hands Collaboration Evaluation for Surgical Skills Assessment: An Information Theoretical Approach. IEEE Trans Med Robot Bionics (2024). Soleymani, A., Li, X. & Tavakoli, M. A domain-adapted machine learning approach for visual evaluation and interpretation of robot-assisted surgery skills. IEEE Robot Autom Lett 7 , 8202–8208 (2022). Pavlidis, M. Two-Dimensional Cutting (TDC) Vitrectome: In Vitro Flow Assessment and Prospective Clinical Study Evaluating Core Vitrectomy Efficiency versus Standard Vitrectome. J Ophthalmol 2016 , 3849316 (2016). Additional Declarations No competing interests reported. Supplementary Files Dataset.csv Cite Share Download PDF Status: Published Journal Publication published 12 Dec, 2025 Read the published version in Scientific Reports → Version 1 posted Editorial decision: Revision requested 26 Aug, 2025 Reviews received at journal 22 Aug, 2025 Reviews received at journal 19 Aug, 2025 Reviews received at journal 14 Aug, 2025 Reviews received at journal 14 Aug, 2025 Reviewers agreed at journal 12 Aug, 2025 Reviews received at journal 10 Aug, 2025 Reviewers agreed at journal 10 Aug, 2025 Reviewers agreed at journal 07 Aug, 2025 Reviewers agreed at journal 07 Aug, 2025 Reviewers agreed at journal 07 Aug, 2025 Reviewers invited by journal 04 Jun, 2025 Editor assigned by journal 04 Jun, 2025 Editor invited by journal 13 May, 2025 Submission checks completed at journal 12 May, 2025 First submitted to journal 10 May, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Toosi University of Technology","correspondingAuthor":false,"prefix":"","firstName":"Mohammad","middleName":"Javad","lastName":"Ahmadi","suffix":""},{"id":467002533,"identity":"089dcd56-1788-4f32-87c5-73e9859a1b6b","order_by":6,"name":"Maryam Mohammadzadeh","email":"","orcid":"","institution":"Translational Ophthalmology Research Centre, Farabi Eye Hospital","correspondingAuthor":false,"prefix":"","firstName":"Maryam","middleName":"","lastName":"Mohammadzadeh","suffix":""},{"id":467002534,"identity":"4a9b71b2-9ab3-4d4b-8343-ecd701318cbf","order_by":7,"name":"Hamid Riazi-Esfahani","email":"","orcid":"","institution":"Translational Ophthalmology Research Centre, Farabi Eye Hospital","correspondingAuthor":false,"prefix":"","firstName":"Hamid","middleName":"","lastName":"Riazi-Esfahani","suffix":""},{"id":467002536,"identity":"fd2b1aa4-7af7-48df-959d-8bc7e3ac0ac2","order_by":8,"name":"Alireza Lashay","email":"","orcid":"","institution":"Translational Ophthalmology Research Centre, Farabi Eye Hospital","correspondingAuthor":false,"prefix":"","firstName":"Alireza","middleName":"","lastName":"Lashay","suffix":""},{"id":467002537,"identity":"c38a2ca1-4773-4e79-810f-f8f159e1182d","order_by":9,"name":"Mohammad Motaharifar","email":"","orcid":"","institution":"University of Isfahan","correspondingAuthor":false,"prefix":"","firstName":"Mohammad","middleName":"","lastName":"Motaharifar","suffix":""},{"id":467002538,"identity":"a8cf0707-dc41-4327-b727-61411a32bafd","order_by":10,"name":"Seyed-Farzad Mohammadi","email":"","orcid":"","institution":"Translational Ophthalmology Research Centre, Farabi Eye Hospital","correspondingAuthor":false,"prefix":"","firstName":"Seyed-Farzad","middleName":"","lastName":"Mohammadi","suffix":""},{"id":467002539,"identity":"c1ad70e8-e347-4613-a120-bf677f01b8a7","order_by":11,"name":"Mahdi Tavakoli","email":"","orcid":"","institution":"University of Alberta","correspondingAuthor":false,"prefix":"","firstName":"Mahdi","middleName":"","lastName":"Tavakoli","suffix":""},{"id":467002540,"identity":"b097935c-4abb-437d-9eb8-2eb9c21748f4","order_by":12,"name":"Hamid D. Taghirad","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA1klEQVRIiWNgGAWjYHACNoYEBgbGfh4Yn5mAeh6Ylpk9DIwNxGsBAsYNZ2BaCAF79uPXHjzcYye7+czZ4495GOzkGdh5H+C3hSen3CDhWbLxtrN9ic08DMmGDczsBgQclpMmkXCAOXHbeR5DoBbmBAZmNgJ+4X8D0lKfuLkfrKWeCC0S6ceAWg4nbuDtAWk5TISWG2/YgFqOG884c8Zw5hyD44ZthLSw96c/k/xxoFq2vyfH4MObimp5fv5j+LUA7UEOHwNoNBGw5wFhNaNgFIyCUTCyAQATPj2tzsWESQAAAABJRU5ErkJggg==","orcid":"","institution":"K. N. Toosi University of Technology","correspondingAuthor":true,"prefix":"","firstName":"Hamid","middleName":"D.","lastName":"Taghirad","suffix":""}],"badges":[],"createdAt":"2025-05-10 12:08:20","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6634699/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6634699/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s41598-025-30688-w","type":"published","date":"2025-12-12T15:58:18+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":84491905,"identity":"88721f62-79a2-4370-a7a8-9ee06a0497f1","added_by":"auto","created_at":"2025-06-12 14:52:20","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":2443589,"visible":true,"origin":"","legend":"\u003cp\u003ea) A comprehensive array of ophthalmic microsurgical instruments, including forceps, scissors, caliper, surgical clamps, and Vicryl suture, meticulously arranged for surgery. b) A cadaveric human globe, used in the study, originally stored at the Farabi Eye Bank and depicted during the warming process to room temperature to prevent thermal shock. c) The ARASH: ASiST haptic system with its 3-DOF table, positioned in Farabi VR surgical operating room, configured for pars plana vitrectomy. d) A portable laser calibration system for precise RCM adjustment, with smoke enhancement for beam visualization, alongside an anatomically accurate ocular phantom. e) High-magnification intraoperative view through the operating microscope, capturing the pars plana trocar for placement of irrigation cannula to maintain intraocular pressure. f) Keratoprosthesis procedure, essential for achieving optimal posterior segment visualization in cases of significant corneal haziness. g) Close-up view of an experienced VR surgeon performing microincision vitrectomy using the ARASH: ASiST haptic system on a donor eye. h) The primary surgeon and scrub team engaged in a complex VR procedure. i) Intraoperative RCM recalibration using the laser system, crucial for accessing the peripheral retina without inducing undue stress on the sclerotomies.\u003c/p\u003e","description":"","filename":"Figure1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6634699/v1/372e7d329f81108893562b11.jpg"},{"id":84491914,"identity":"65c4821b-6397-46fa-a021-bc2532207278","added_by":"auto","created_at":"2025-06-12 14:52:20","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":12731169,"visible":true,"origin":"","legend":"\u003cp\u003eOperation Room Plan for Clinical Test\u003c/p\u003e","description":"","filename":"Figure2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6634699/v1/585c58efe035f88adf010faf.jpg"},{"id":84492885,"identity":"91be7401-0bb1-4d3f-9846-bc910b550ea9","added_by":"auto","created_at":"2025-06-12 15:00:20","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":254068,"visible":true,"origin":"","legend":"\u003cp\u003e5-point Likert scale to assess the level of comfort across multiple aspects of the surgical setup and procedure\u003c/p\u003e","description":"","filename":"Figure3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6634699/v1/d5c6e0e19e44a8a41277c259.jpg"},{"id":84491909,"identity":"9f16330a-31c8-4a27-bd1c-2d9cd4566c77","added_by":"auto","created_at":"2025-06-12 14:52:20","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":346624,"visible":true,"origin":"","legend":"\u003cp\u003ePVD induction (DW method) trajectories viewed from different perspectives. (a) and (b) show the trajectory for the right-handed expert surgeon from front and rotated perspectives, respectively, highlighting the observed DW pattern. (c) and (d) illustrate the trajectory for the left-handed intermediate surgeon from similar perspectives.\u003c/p\u003e","description":"","filename":"Figure4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6634699/v1/e58886c76c150deea9aa8168.jpg"},{"id":84491933,"identity":"05b238b3-2a94-4917-b94f-cc522422c475","added_by":"auto","created_at":"2025-06-12 14:52:21","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":776371,"visible":true,"origin":"","legend":"\u003cp\u003eForce distribution during PVD induction for each surgical procedure. 'RH' and 'LH' indicate operations performed with the right hand and left hand, respectively. The data for the intermediate surgeon includes two measurements for RH and two for LH.\u003c/p\u003e","description":"","filename":"Figure5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6634699/v1/3484f1b34ffdff36eefebc32.jpg"},{"id":84491910,"identity":"55cc9243-ace8-4122-9f46-3c25b5edb33d","added_by":"auto","created_at":"2025-06-12 14:52:20","extension":"jpg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":450169,"visible":true,"origin":"","legend":"\u003cp\u003eTime distribution in the PVD induction stages for each surgical procedure.\u003c/p\u003e","description":"","filename":"Figure6.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6634699/v1/8af6d3987eb2432c8a2baf37.jpg"},{"id":98244306,"identity":"6b40f0f6-edcc-4808-9175-d7ab96641fd7","added_by":"auto","created_at":"2025-12-15 16:14:04","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":17562782,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6634699/v1/bcda3ab5-6eff-4497-9835-78328f1b68c5.pdf"},{"id":84493538,"identity":"7c66a5c4-fd78-431e-8be5-2637b9c5636e","added_by":"auto","created_at":"2025-06-12 15:08:20","extension":"csv","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":6027411,"visible":true,"origin":"","legend":"","description":"","filename":"Dataset.csv","url":"https://assets-eu.researchsquare.com/files/rs-6634699/v1/3be20680f51be2a297ede69d.csv"}],"financialInterests":"No competing interests reported.","formattedTitle":"Evaluating Force and Motion in Posterior Vitreous Detachment Manoeuvres Using a Robotic Data Acquisition System in Cadaveric Human Eyes","fulltext":[{"header":"Introduction","content":"\u003cp\u003eModern surgical data acquisition (DAQ) systems enable precise recording and analysis of surgical techniques through multimodal sensing technologies\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e. Among these, force and positional measurements provide critical intuition into technical proficiency through the high-precision capture of key surgical metrics, such as instrument trajectories\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e and hand forces\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e. Historically, operating room (OR) data collection depended on subjective observations and debriefing checklists, which were often\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e prone to biases and lacked scalability\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e. Despite advancements in DAQ systems, many existing solutions disrupt surgical workflows and fail to preserve the intricate precision of ophthalmic techniques. This highlights the need for non-intrusive approaches that align seamlessly with the demands of microsurgical procedures.\u003c/p\u003e \u003cp\u003eTo address this challenge, we present ARASH:ASiST, a transparent robotic platform specifically designed for VR microsurgery. While primarily enhancing surgical precision and control through tactile exchange during vitrectomy learning and teaching, it can also be used for seamless and authentic intraoperative data collection for objective skills assessment. Through pre-clinical validation, ARASH:ASiST demonstrated its ability to capture delicate surgical movements and provide objective analysis across various stages of vitrectomy procedure. Recent advancements in AI-driven surgical skill assessment have demonstrated the potential for automated and objective evaluation of surgeon expertise through robotic data acquisition, reducing the subjectivity inherent in traditional assessment methods\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e. The ARASH:ASiST system aligns with these developments by providing force and trajectory-based performance metrics that can be used for skill evaluation and training. Its Graphical User Interface (GUI) further supports detailed visualization of surgeons' movements, facilitating comparisons of skill levels among practitioners. This system not only advances our understanding of surgical techniques and psychomotor demands but also represents a pivotal step toward developing comprehensive databases of best practices, ultimately enhancing precision, reducing errors, and improving surgical outcomes.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eTissue Preparation\u003c/h2\u003e \u003cp\u003eWe used postmortem human cadaveric eyes, which has been stored in a lab fridge at -20\u0026deg;C in the Eye Bank of Farabi Eye Hospital (Tehran, Iran). Globes were obtained from a 23-year-old Iranian male cadaver, which tested positive for Human Immunodeficiency Virus, ineligible for corneal transplantation and used in the mechanical test session, and a 47-year-old female cadaver, positive for Hepatitis C Virus, which was engaged in the DAQ session. The tissues were maintained in a sealed iced package the day prior to the experiment (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.b). Tissues were brought up to room temperature, in order to prevent tissue from temperature shock prior to placing in the holder.\u003c/p\u003e\u003cp\u003e\u003cem\u003eEthical Approval\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe study protocol was approved by the Research Ethics Committee of Tehran University of Medical Sciences (IR.TUMS.FARABIH.REC.1395.4) and National Institute for Medical Research Development (IR.NIMAD.REC.1395.032).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eVitrectomy\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe cadaveric eye was placed on an eye holder fixed to a mannequin head and held in place by the negative pressure of the eye holder\u003csup\u003e6\u003c/sup\u003e. The cadaveric eye and the eye holder were then positioned in the right orbit of the mannequin head. The surgeon sat superior to the mannequin head after draping field, imitating a sterile condition. Three ports of 23-gauge valved trocars were placed 3.5 mm off the limbus: one in the inferotemporal quadrant for the irrigation cannula, one in the superotemporal quadrant, and one in the superonasal quadrant for the light port and vitrectomy probe. The irrigation pressure was set to 30 cm of water height to maintain the intraocular pressure.\u003c/p\u003e\n\u003cp\u003eAn anterior segment surgeon [SFM] removed the cadaver cornea using an 8\u003cspan dir=\"RTL\"\u003e\u0026nbsp;\u003c/span\u003emm vacuum trephine. The crystalline lens was also removed open-sky and delicately to enhance the view of the posterior segment. A temporary PMMA keratoprosthesis (KPro) with trunk was then sutured to the bed with interrupted 8-0 Vicryl sutures. Visualization of the posterior segment was provided by the Haag-Streit surgical microscope enhanced with a 90-diopter BIOM\u0026reg; lens (HS Hi-R NEO 900A, Haag-Streit, Wedel, Germany) and the Haag-Streit C.MOR HD surgical microscope camera and recording system.\u003c/p\u003e\n\u003cp\u003eThe experienced retina surgeon [HR] performed the deep vitrectomy act from the other two trocar ports using the DORC EVA vitrectomy machine (vitreous cutter; Dutch Ophthalmic Research Centre, Zuidland, Netherlands). The vitrectomy probe was set with a cut rate of 3500 cuts per minute and a vacuum of 300 mmHg. It was attached to the haptic device, while held and directly operated by the surgeon. The retina surgeon acted core vitrectomy in all quadrants of the vitreous cavity. Core vitrectomy is a surgical procedure performed to remove the core vitreous gel from the central part of the posterior segment of the eye in all quadrants.\u003c/p\u003e\n\u003cp\u003eAs mentioned, the cadaveric eye had been held frozen for more than one year and we found total retinal detachment. The detached peripheral retina was removed first with the vitrectome probe to facilitate performing peripheral vitreous shaving. To carry out these tasks, the surgeon [HR] moved the probe circumferentially, covering the entire peripheral retina, while also rotating the eye to visualize the periphery, ensuring adequate exposure of the farthest internal periphery of the cadaveric eye. The feedback from the surgeon regarding the ease of these manoeuvres was recorded. To assess the ease of handling the vitrectomy probe during the procedure and peripheral vitreous shaving on the right socket of the mannequin head, two different retina surgeons, one right-handed [HR] and one left-handed [EA], performed the procedures. The vitrectomy probe was mounted with the robot, allowing movement in all possible directions during vitrectomy. The objective was to record and compare the ease of handling the probe for both surgeons, considering their dominant hand preferences.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eHaptic System\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe proposed haptic system, ARASH:ASiST, has transferred intraoperative surgical data collection and analysis. Through its vitrectomy probe-like interface, precise positional and force data from surgeons are captured in the OR\u0026mdash;a capability previously unattainable without significantly disrupting the natural workflow of VR surgery.\u003c/p\u003e\n\u003cp\u003eIn vitrectomy and other minimally invasive surgery (MIS) procedures, instruments access the target organ through small incisions. The endoilluminator and primary instruments, such as the vitrector, are inserted through trocar ports. The haptic system\u0026rsquo;s end effector, which houses the vitrector, provides three rotational and one translational degree of freedom (DoF). These DoFs intersect at the insertion port to create a remote centre of motion (RCM), generating a spherical sector workspace. Mechanically engineered via a parallelogram mechanism, this RCM configuration simplifies kinematics, enables near-decoupled dynamics, and enhances structural rigidity and manoeuvrability. Consequently, surgeons can manipulate the instrument with improved ease and precision.\u003c/p\u003e\n\u003cp\u003eIn the design of ARASH:ASiST for minimally invasive ophthalmic surgery, several critical features optimize performance, including low inertia, zero backlash, and full back-drivability achieved through a capstan drive system. Lightweight aluminium links, strategic mass balancing, and compact cable-pulley mechanisms enhance precision, agility, and reliability. Ergonomic considerations minimize surgeon fatigue, while easy maintenance and RCM calibration ensure consistent performance and safety. To optimize the surgical field and positioning, the system features an anatomically accurate eye holder for realistic tissue manipulation and a custom 3-DoF table for seamless integration with perioperative equipment. This setup ensures precise alignment of the robot\u0026apos;s RCM with the sclerotomy port, enabling accurate and stable instrument entry into the globe.\u003c/p\u003e\n\u003cp\u003eThe ARASH:ASiST system adopts an innovative approach by embedding data acquisition hardware directly into the robot structure, eschewing conventional commercial peripheral component interconnect (PCI) cards. This strategy offers cost-effectiveness and a unified hardware framework throughout development and production. An Ethernet interface facilitates user datagram protocol (UDP) communication between the embedded controller and target computer. The system\u0026apos;s core comprises an ARM CORTEX-M7 microcontroller, serving as a data bridge and executing computationally intensive controllers and observers. The 1 kHz sampling rate of the communication link suffices for the robot\u0026apos;s rapid transient responses. Control commands from the target computer are routed to a serial peripheral interface (SPI) digital to analogue converter (DAC) chip, generating analogue torque commands for the motor driver in current control mode. Simultaneously, the microcontroller interprets motor encoder signals and relays them to the target computer, establishing a closed-loop control system.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eGraphical User Interface (GUI)\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eA GUI was developed for the ARASH:ASiST and implemented during the DAQ session\u003csup\u003e7\u003c/sup\u003e. The interface features a schematic eye model, created using computer-aided design (CAD) software based on average anatomical parameters\u003csup\u003e8\u003c/sup\u003e. This model serves as an interactive component within the GUI, with the surgical trajectory displayed relative to the eye model to provide a realistic, real-time visualization. Additionally, the GUI visualizes applied force by translating measurements from the haptic system into color-coded trajectories.\u003c/p\u003e\n\u003cp\u003eTo achieve precise calibration of the eye model to match the dimensions of the cadaveric eye, an expert surgeon accessed the optic nerve head while kinematic data from the haptic system was calculated in real time. This data was used to determine the exact distance between the trocar port and optic nerve head, which was then set in the GUI. After calibration, the minimal remaining error was solely due to forward kinematic approximations and subtle variations in the surgeon\u0026rsquo;s skill, ensuring that the 3D model remained aligned with the anatomical dimensions throughout the session.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eData Acquisition (DAQ) Protocol\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eTrajectory-based analysis has been recognized as a key method for evaluating surgical proficiency, as it enables the identification of skill-related features such as smoothness, efficiency, and motion economy\u003csup\u003e9\u003c/sup\u003e. The ARASH:ASiST system builds upon this approach by capturing real-time force and positional data to quantify surgeon expertise. A detailed DAQ instruction guide was prepared, offering an overview of previous clinical tests, integrating relevant feedback, and outlining the objectives of the DAQ session. The guide was shared with surgeons and attendees prior to the event. During the session, data was systematically recorded by each surgeon across ten subphases of deep vitrectomy, categorized into three procedural stages: \u003cem\u003eEntry and Initial Handling\u003c/em\u003e, \u003cem\u003eCore Vitrectomy\u003c/em\u003e, and \u003cem\u003ePVD Induction\u003c/em\u003e.\u003c/p\u003e\n\u003cp\u003eThe subphases included: \u003cem\u003eEntry, Local vitrectomy (near trocar ports), Anterior Core (removal of vitreous posterior to the pupil), Inferonasal, Inferotemporal, Superonasal, Superotemporal quadrant core vitrectomy, PVD induction, and Exit\u003c/em\u003e.\u003c/p\u003e\n\u003cp\u003eDuring the PVD induction, surgeons employed the drunk walk (DW) technique\u003csup\u003e10\u003c/sup\u003e. This method involves placing the probe near the retinal surface adjacent to the optic disc and applying vacuum suction to engage the vitreous body. The probe is then advanced in a controlled zigzag motion along the expected path of the inferotemporal vascular arcade and anteriorly, facilitating detachment of the posterior vitreous from the retina. The technique was specifically adapted to address the total retinal detachment in the cadaveric eye, with manoeuvres guided by anatomical landmarks and expected structural patterns.\u003c/p\u003e\n\u003cp\u003eThroughout the operation, the vitrectomy probe was positioned with the cutter facing upward to ensure visibility of the cutting portion.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eDuring an initial pre-clinical test, we evaluated the haptic design and mechanical performance of the system (Fig. 1), confirming its effectiveness in resolving surgeon-reported issues. Building on this, a DAQ session was conducted during vitrectomy acts (Fig. 2). To ensure structured data collection, the DAQ protocol guided attendees through the procedures in the OR at Farabi Hospital.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eSystem Integration and Workflow Compatibility\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eTo assess the ARASH:ASiST system\u0026rsquo;s capacity to integrate seamlessly into the OR without disrupting natural surgical workflows, eight domain-specific features were evaluated: 1) integration within OR environment (workflow synchronization), 2) sterilization (adherence to aseptic protocols), 3) OR equipment positioning (compatibility with standard layouts), 4) eye holder (anatomic fixation fidelity), 5) mannequin head (procedural authenticity), 6) robotic system interface (instrument control responsiveness), 7) robot table (positioning flexibility), and 8) GUI (real-time data visualization).\u003c/p\u003e\n\u003cp\u003eA cohort of 14 preclinical users\u0026mdash;comprising two VR fellows, two intermediate VR surgeons, two experienced VR surgeons, two cornea surgeons, three research assistants, and three OR technicians\u0026mdash;completed a postoperative questionnaire. The questionnaire utilized a 5-point Likert scale to assess the level of comfort across multiple aspects of the surgical setup and procedure, ranging from 1 (Extremely Uncomfortable) to 5 (Extremely Comfortable), as shown in\u0026nbsp;Fig. 3. Feedback was stratified by role to ensure clinical relevance: VR surgeons evaluated all eight features, while cornea surgeons assessed five features (excluding robotic interface, robot table, and GUI). Support staff (assistants/technicians and researchers) focused on three domains critical to workflow efficiency: sterilization, OR equipment positioning, and integration within the OR environment.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eVitrectomy Data\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe dataset comprises seven surgical recordings under standardized conditions, including contributions from two fellows (\u0026lt;2 years of experience), one intermediate surgeon (\u0026gt;4 years), and one expert (\u0026gt;8 years). The intermediate surgeon acted four procedures: two on the mannequin\u0026rsquo;s right eye (right-handed) and two on the left eye (left-handed). All utilized the ARASH:ASiST system, with instrument kinematics (Encoder1/2 in degrees, Linear Encoder3 in mm) and forces (Newtons) recorded at 1 kHz following initial calibration.\u003c/p\u003e\n\u003cp\u003eDuring the operation, the eye is often tilted to provide better visualization and access to specific areas of the posterior segment. The collected data reveals that in certain phases, specifically the DW method, where access to the optic nerve is required, this tilting is particularly pronounced. Notably, in the data from the left-handed surgeon, the eye is slightly tilted to enhance access and visibility, as shown in Fig. 4.c.\u003c/p\u003e\n\u003cp\u003eThe DW method, as the name suggests, mimics the staggered movement characteristic of a person walking under the influence of alcohol. This distinctive trajectory is evident in the 3D plots of the surgeon\u0026apos;s movements, where it appears as a unique pattern, as shown in Fig. 4.b and Fig. 4.d.\u003c/p\u003e\n\u003cp\u003eThe recorded forces in the haptic system ranged from -3 N to +6 N across the entire operation and all recorded data. The sign of the force reflects opposing movements within the haptic system. This range varied across different subphases of the procedure. For example, during the DW Method, the force data patterns enabled the differentiation of surgeons, based on their dominant hand. The force range for right-dominant surgeons spanned from -0.5 N to 5.89 N, while the left-dominant surgeon exhibited a narrower range from -2.96 N to -0.7 N. These differences are also evident in the trajectory visualizations, where positive forces are represented by yellow to red hues and negative forces by shades of purple. The trajectory of the left-dominant surgeon remained entirely within the purple spectrum, whereas right-dominant surgeons displayed trajectories in yellow to red, as shown in Fig. 4.\u003c/p\u003e\n\u003cp\u003eHand-dominance has been found to influence robotic-assisted surgical performance, with measurable differences in trajectory control and force application between right-handed and left-handed surgeons\u003csup\u003e11\u003c/sup\u003e. Our findings confirm this effect, as demonstrated by the distinct force patterns observed during posterior vitreous detachment induction. The two surgical fellows in the study were right-dominant and consistently demonstrated significantly higher force values during the DW subphase of the operation. This contributed to a broader force range observed across all surgical data, with the positive limit extending to 5.2 N and the negative limit confined to -2 N, as shown in Fig. 5. The elevated positive force values reflect the fellows\u0026apos; techniques, in contrast to the intermediate left-dominant surgeon, whose lower force values resulted in a narrower negative range. Additionally, when comparing the force ranges of the expert surgeon during this phase, it becomes evident that the fellows\u0026apos; less advanced techniques led to the application of forces that were substantially higher than necessary for a precise and controlled operation. This highlights how variations in expertise and technique influence force application during specific surgical phases. The ground truth in this analysis is based on the level of experience of each surgeon.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn this study, all seven surgical act datasets were recorded on the same cadaveric eye. The initial procedure presented challenges due to the presence of vitreous and retinal detachment, which impacted the surgical environment. However, the removal of the vitreous during the first procedure established consistent conditions for all subsequent recordings. The objective was to systematically navigate the posterior segment of the eye, phase by phase, in accordance with the DAQ protocol.\u003c/p\u003e\n\u003cp\u003eDespite variations in individual surgical techniques and preferences, certain subphases, such as the DW method, follow predefined trajectories that enable comparative analysis across surgeons. This consistency provides a foundation for extracting novel insights from the recorded force and positional data, which can be used to analyse specific aspects of surgical performance. These findings underscore the potential of the collected data to deepen our understanding of vitrectomy procedures, offering valuable perspectives on both the operation itself and the application of haptics and robotics in VR surgery, its training and assessment.\u003c/p\u003e\n\u003cp\u003eCombining domain knowledge-based metrics with machine learning techniques has been shown to improve the accuracy and interpretability of surgical skill assessments\u003csup\u003e12\u003c/sup\u003e. Our study leverages this hybrid approach by integrating real-time force and trajectory measurements with expert evaluations to create a more comprehensive skill assessment framework.\u003c/p\u003e\n\u003cp\u003eThe duration of each phase is a crucial factor in evaluating surgical performance. However, a shorter phase duration does not necessarily indicate superior performance, as precision and thoroughness are essential for achieving optimal outcomes. Interestingly, in some subphases, the fellows exhibit shorter phase durations compared to the intermediate and expert surgeons. However, for the DW method, the time to completion is approximately 27 seconds for the expert surgeon, 36 seconds for the intermediate surgeon, and 59 seconds for the fellows. This trend highlights the balance between time and technique in this particular subphase, emphasizing that while efficiency is valuable, the approach of experienced surgeons ensures both accuracy and control while completing the task within a reasonable time frame.\u003c/p\u003e\n\u003cp\u003eThe time spent in the DW method procedure is divided into three stages. The Approaching Stage, which involves reaching the optic nerve; the Operational Stage, which includes the DW method procedure itself; and the Receding Stage, which represents the final movement of receding from the main operating region. These stages are illustrated in Fig. 6.\u003c/p\u003e\n\u003cp\u003eThe DORC EVA machine, an advanced aspiration system enabling both flow and vacuum control modes, was employed during data collection10. This platform is designed to enhance intraoperative fluidic stability, allowing for precise control of intraocular pressure, which was critical in maintaining the stability of the cadaveric eye. The flow control capability of the DORC EVA machine facilitated consistent pressure regulation, tailored to the experimental requirements, enabling the successful acquisition of seven surgical data from a single cadaveric eye. However, minor variations in the flow dynamics across datasets led to subtle changes in the eyeball\u0026apos;s shape and dimensions over time. These slight alterations introduced a small range of variability in the data. Given the accurate calibration of the haptic system, the observed differences are attributed to these minor variations in the intraocular fluid dynamics rather than the instrumentation inconsistencies. This underscores the inherent sensitivity of cadaveric tissue to procedural conditions, even in highly controlled settings.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eGUI Feedback\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe GUI provided real-time feedback, serving as a valuable tool for the observing surgeons\u0026rsquo; performance. While the operating surgeon remained focused on the procedure through the microscope and did not directly utilize the GUI during the surgery, it proved highly beneficial for the observers. The GUI offered clear visual feedback cues, effectively indicating the current stage of the operation (Fig. 4).\u003c/p\u003e\n\u003cp\u003eThe observing surgeons were able to monitor the operating surgeon\u0026apos;s approach to each section of the posterior segment, including specific techniques such as PVD induction. This facilitated enhanced communication and skill transfer, allowing for collaborative guidance during the procedure. Surgeons could discuss and refine techniques in real time, fostering an interactive learning environment. Furthermore, the GUI data could be reviewed postoperatively, offering valuable insights for analysis and skill development. In the standard feedback framework, the observation is through accessory teaching oculars or the online video screen which streams the surgical procedure.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eLimitations and Future Perspective\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThis study, while providing foundational insights into robotic-assisted VR surgical data acquisition, is subject to an inherent limitation due to its preclinical nature. The absence of an intact vitreous body in cadaveric eyes precluded evaluation of surgical steps efficiency under real-world clinical conditions, and postmortem retinal detachment necessitated removal of the peripheral retina, altering the procedural context compared to a real surgery. Additionally, there is no universal deep vitrectomy manoeuvres set, as steps were surgeon-dependent; not all phases of a complete vitrectomy were performed either. The small cohort size further limited normative data collection across experience levels and handedness, underscoring the need for broader validation, for instance by including mid- to late carrier VR surgeons.\u003c/p\u003e\n\u003cp\u003eTechnically-speaking, we collected the axial and lateral driving force, positional/trajectory, and temporal data of simulated deep vitrectomy procedure by the ARASH:ASiST system. In real vitrectomy surgery there is another key element, i.e. rotational dynamism of the vitrectomy probe. ARASH:ASiST is not able to register this aspect which constitutes an integral element of surgical efficiency and adroitness.\u003c/p\u003e\n\u003cp\u003eFuture efforts will prioritize expanding the dataset to address these constraints. Physiologically realistic ocular phantoms with synthetic vitreous can be employed to replicate in vivo conditions. Procedural variability can be minimized through pre-trial discussion and agreements. In a multi-institutional collaboration we can recruit a larger cohort of surgeons stratified by experience (na\u0026iuml;ve to expert) and handedness, ensuring robust normative benchmarks. Synchronization of the ARASH:ASiST kinematic and force data with vitrectomy machine parameters (cut rate, vacuum, etc.) and intraoperative imaging will enable multimodal analytics, refining skill assessment frameworks.\u003c/p\u003e\n\u003cp\u003eThis enriched dataset will underpin AI-driven performance evaluations, generating surgeon-specific feedback reports to personalize training. It is note-worthy that the ARASH:ASiST is basically a dual haptic system for real-time haptic exchange. Our current report proves the concept that surgical education can be further enhanced, not only by haptic feedback but also by a robotic data acquisition system. These will finally, improve patient safety and microsurgical training efficiency.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 21.9634%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAbbreviation\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78.0366%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDescription\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 21.9634%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDAQ\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78.0366%;\"\u003e\n \u003cp\u003eData Acquisition\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 21.9634%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eOR\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78.0366%;\"\u003e\n \u003cp\u003eOperating Room\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 21.9634%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eARASH:ASiST\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78.0366%;\"\u003e\n \u003cp\u003eARAS HAptic for EYE Surgery Training\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 21.9634%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eGUI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78.0366%;\"\u003e\n \u003cp\u003eGraphical User Interface\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 21.9634%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eKPro\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78.0366%;\"\u003e\n \u003cp\u003eKeratoprosthesis\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 21.9634%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMIS\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78.0366%;\"\u003e\n \u003cp\u003eMinimally Invasive Surgery\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 21.9634%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDoF\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78.0366%;\"\u003e\n \u003cp\u003eDegree of Freedom\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 21.9634%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eRCM\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78.0366%;\"\u003e\n \u003cp\u003eRemote Centre of Motion\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 21.9634%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eWC\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78.0366%;\"\u003e\n \u003cp\u003eWeight Compensation\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 21.9634%;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePCI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78.0366%;\"\u003e\n \u003cp\u003ePeripheral Component Interconnect\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 21.9634%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eUDP\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78.0366%;\"\u003e\n \u003cp\u003eUser Datagram Protocol\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 21.9634%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSPI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78.0366%;\"\u003e\n \u003cp\u003eSerial Peripheral Interface\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 21.9634%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDAC\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78.0366%;\"\u003e\n \u003cp\u003eDigital to Analogue Converter\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 21.9634%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCAD\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78.0366%;\"\u003e\n \u003cp\u003eComputer-Aided Design\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 21.9634%;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePVD\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78.0366%;\"\u003e\n \u003cp\u003ePosterior Vitreous Detachment\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 21.9634%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDW\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78.0366%;\"\u003e\n \u003cp\u003eDrunk Walk\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 21.9634%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eVR\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78.0366%;\"\u003e\n \u003cp\u003eVitreoretinal\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eFinancial Support:\u003c/strong\u003e This work partially funded by the Iran National Science Foundation (INSF) (No.4024081), and The National Centre for Strategic Research in Medical Education (NASR)\u0026nbsp;(No.\u0026nbsp;4010324). The sponsors or funding organizations had no role in the design or conduct of this research.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of Interest:\u003c/strong\u003e No conflicting relationship exists for any author.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets used and/or analysed during the current study available from the corresponding author on reasonable request.\u003c/p\u003e\n"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eLevin, M. \u003cem\u003eet al.\u003c/em\u003e Surgical data recording in the operating room: a systematic review of modalities and metrics. \u003cem\u003eBritish Journal of Surgery\u003c/em\u003e \u003cstrong\u003e108\u003c/strong\u003e, 613\u0026ndash;621 (2021).\u003c/li\u003e\n\u003cli\u003eHubschman, J. P., Son, J., Allen, B., Schwartz, S. D. \u0026amp; Bourges, J. L. Evaluation of the motion of surgical instruments during intraocular surgery. \u003cem\u003eEye\u003c/em\u003e \u003cstrong\u003e25\u003c/strong\u003e, 947\u0026ndash;953 (2011).\u003c/li\u003e\n\u003cli\u003eTeixeira, A. \u003cem\u003eet al.\u003c/em\u003e Vitreoretinal traction created by conventional cutters during vitrectomy. \u003cem\u003eOphthalmology\u003c/em\u003e \u003cstrong\u003e117\u003c/strong\u003e, 1387\u0026ndash;1392 (2010).\u003c/li\u003e\n\u003cli\u003eSoleymani, A., Li, X. \u0026amp; Tavakoli, M. Artificial intelligence in robot-assisted surgery: Applications to surgical skills assessment and transfer. \u003cem\u003eMedical and Healthcare Robotics\u003c/em\u003e 183\u0026ndash;200 (2023).\u003c/li\u003e\n\u003cli\u003eMcQueen, S. A. \u003cem\u003eet al.\u003c/em\u003e Examining the barriers to meaningful assessment and feedback in medical training. \u003cem\u003eThe American Journal of Surgery\u003c/em\u003e \u003cstrong\u003e211\u003c/strong\u003e, 464\u0026ndash;475 (2016).\u003c/li\u003e\n\u003cli\u003eMohammadi, S. F., Mazouri, A., Rahman-A, N., Jabbarvand, M. \u0026amp; Peyman, G. A. Globe-fixation system for animal eye practice. \u003cem\u003eJ Cataract Refract Surg\u003c/em\u003e \u003cstrong\u003e37\u003c/strong\u003e, 4\u0026ndash;7 (2011).\u003c/li\u003e\n\u003cli\u003eAghapour, M., Heidari, R., Nazeri, M. M., Ahmadi, M. J. \u0026amp; Taghirad, H. D. Development and Integration of an Advanced GUI with Real-Time Data Logging for RCM-Based Robotic Surgery Devices. \u003cem\u003eICRoM 2024 - 12th RSI International Conference on Robotics and Mechatronics\u003c/em\u003e 426\u0026ndash;430 (2024) doi:10.1109/ICROM64545.2024.10903536.\u003c/li\u003e\n\u003cli\u003eYang, Y. \u003cem\u003eet al.\u003c/em\u003e Safety control method of robot-assisted cataract surgery with virtual fixture and virtual force feedback. \u003cem\u003eJ Intell Robot Syst\u003c/em\u003e \u003cstrong\u003e97\u003c/strong\u003e, 17\u0026ndash;32 (2020).\u003c/li\u003e\n\u003cli\u003eSoleymani, A., Li, X. \u0026amp; Tavakoli, M. Surgical procedure understanding, evaluation, and interpretation: A dictionary factorization approach. \u003cem\u003eIEEE Trans Med Robot Bionics\u003c/em\u003e \u003cstrong\u003e4\u003c/strong\u003e, 423\u0026ndash;435 (2022).\u003c/li\u003e\n\u003cli\u003eStorey, P. P. \u0026amp; Garg, S. J. The Drunk Walk Method for Posterior Vitreous Detachment Induction. \u003cem\u003eAmerican Academy of Ophthalmology\u003c/em\u003e (2019).\u003c/li\u003e\n\u003cli\u003eSoleymani, A. \u003cem\u003eet al.\u003c/em\u003e Hands Collaboration Evaluation for Surgical Skills Assessment: An Information Theoretical Approach. \u003cem\u003eIEEE Trans Med Robot Bionics\u003c/em\u003e (2024).\u003c/li\u003e\n\u003cli\u003eSoleymani, A., Li, X. \u0026amp; Tavakoli, M. A domain-adapted machine learning approach for visual evaluation and interpretation of robot-assisted surgery skills. \u003cem\u003eIEEE Robot Autom Lett\u003c/em\u003e \u003cstrong\u003e7\u003c/strong\u003e, 8202\u0026ndash;8208 (2022).\u003c/li\u003e\n\u003cli\u003ePavlidis, M. Two-Dimensional Cutting (TDC) Vitrectome: In Vitro Flow Assessment and Prospective Clinical Study Evaluating Core Vitrectomy Efficiency versus Standard Vitrectome. \u003cem\u003eJ Ophthalmol\u003c/em\u003e \u003cstrong\u003e2016\u003c/strong\u003e, 3849316 (2016).\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Vitreoretinal Surgery, Surgical Data Acquisition, Cadaveric Human Eyes","lastPublishedDoi":"10.21203/rs.3.rs-6634699/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6634699/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eDeep vitrectomy, a complex ophthalmic procedure, requires significant surgical skill. This study evaluated the ARASH:ASiST robotic system for real-time, quantifiable assessment of deep vitrectomy in a pre-clinical setting using cadaveric human eyes. Four surgeons, with varying experience levels, performed procedures while intraoperative force, positional, and temporal data were recorded. Analysis of seven surgical datasets revealed experience-specific differences in force application, particularly during posterior vitreous detachment induction, which emerged as a key performance metric. Time data provided insights into surgeon control. The system's real-time graphical user interface offered immediate feedback and facilitated postoperative analysis. Usability assessments confirmed the system's practicality and non-intrusiveness. 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