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. Author manuscript; available in PMC: 2020 Nov 1.
Published in final edited form as: Annu Int Conf IEEE Eng Med Biol Soc. 2020 Jul;2020:5101–5105. doi: 10.1109/EMBC44109.2020.9176853

A Comparison of Manual and Robot Assisted Retinal Vein Cannulation in Chicken Chorioallantoic Membrane

Niravkumar Patel 1, Muller Urias 2,3, Changyan He 4,5, Peter L Gehlbach 6, Iulian Iordachita 7
PMCID: PMC7538656  NIHMSID: NIHMS1590683  PMID: 33019134

Abstract

Retinal vein occlusion (RVO) is a vision threatening condition occurring in the central or the branch retinal veins. Risk factors include but are not limited to hypercoagulability, thrombus or other cause of low blood flow. Current clinically proven treatment options limit complications of vein occlusion without treating the causative occlusion. In recent years, a more direct approach called Retinal Vein Cannulation (RVC) has been explored both in animal and human eye models. Though RVC has demonstrated potential efficacy, it remains a challenging and risky procedure that demands precise needle manipulation to achieve safely. During RVC, a thin cannula (diameter 70–110 μm) is delicately inserted into a retinal vein. Its intraluminal position is maintained for up to 2 minutes while infusion of a therapeutic drug occurs. Because the tool-tissue interaction forces at the needle tip are well below human tactile perception, a robotic assistant combined with a force sensing microneedle could alleviate the challenges of RVC. In this paper we present a comparative study of manual and robot assisted retinal vein cannulation in chicken chorioallantoic membrane (CAM) using a force sensing microneedle tool. The results indicate that the average puncture force and average force during the infusion period are larger in manual mode than in robot assisted mode. Moreover, retinal vein cannulation was more stable during infusion, in robot assisted mode.

I. Introduction

Retinal vein occlusion has a prevalence of 1.8% for central and 0.5% for single branch retinal veins [1] and affects approximately 16.4 million people worldwide [2]. Typical causes for RVO are low blood flow, hyper-coagulability or thrombosis in the retinal veins. Dilated retinal veins due to RVO often become tortuous and cause leakage into the retinal tissue, resulting in blurred and/or distorted vision. Severe cases of RVO may progress to blindness despite best current treatments [3].

Intra-vitreal injections, photocoagulation, radial optic neurotomy, hemodilution, and vitrectomy are some of the currently available treatments for RVO; however, these treatments only limit the damage secondary to the vein occlusion and do not address the underlying occlusion [4]. Delivering therapeutic agents directly into the occluded vein could potentially resolve the occlusion and restore the blood flow. An experimental procedure, retinal vein cannulation in which a small cannula is inserted into the occluded vein to deliver an anticoagulating drug has been recently studied in animal [5] as well as human [6] retinal models. The RVC procedure has three phases: (1) accurately positioning a sharp bevel-tipped prebent cannula onto the occluded retinal vein, (2) puncturing the target vein and achieving a stable intraluminal position, and (3) holding the cannula inside the vein lumen while injecting the therapeutic agent (plasminogen activator (t-PA) or ocriplasmin) to dissolve the obstructing thrombus. Surgeon’s hand tremor on the order of 100 μm [7] has made it challenging to consistently perform such a procedure procedure without complications, especially when the target retinal vein diameter is smaller than 200 μm [8].

Though, RVC has been evaluated for its feasibility, it remains an experimental procedure due to the precise cannula/tool manipulation challenges it poses in the high risk environment of the eye. Demanding requirements for precise tool manipulation has inspired development of robotic assistants with the potential to eliminate hand tremor and enable surgeons to perform microscopic positioning and cannula insertion. At present vitreoretinal surgical systems include the cooperatively controlled steady hand eye robot [9], a master-slave robotic surgical system (IRISS) for intraocular surgical procedures [10], and robotic assistant for vitreoretinal membrane peeling [11]. Recently, Willekens et al. and Gijbels et al. presented robot-assisted retinal vein cannulation in an in-vivo porcine eye [12] and human eye [6] retinal vein occlusion models, respectively.. An alternative approach to the cooperative and tele-manipulated robotic systems, is presented by Riviere et al. in the form of a handheld piezo-actuated eye surgery device, called Micron, which can compensate for surgeon hand tremor [13]. Though, such robotic assistants provide high precision manipulation capabilities and tremor removal/reduction, the tool-tissue interaction forces in retinal interventions remain beyond the limits of human tactile perception. The need for perceiving such tool-tissue interactions motivated the development of force-sensing surgical instruments for retinal surgery [5], [14].

Previously our team presented retinal vein cannulation in artificial phantoms [15] and in the CAM of fertilized chicken embryos [16], but using a handheld robotic assistant. In this work, we present a comparison between the manual and cooperatively controlled robot assisted retinal vein cannulation in the CAM of 13 days old chicken embryos. We assess the tool-tissue interaction forces and their relationship with the vein size in manual and robot assisted vein cannulation experiments.

II. System Setup

The system setup consists of the Steady Hand Eye Robot (SHER) and a FBG-based force sensing cannulation instrument, as shown in Fig. 1. The SHER [9] is a cooperatively controlled robotic assistant with high precision joint motions that allow interventions on a small scale required for retinal surgeries. SHER has a 6 degrees of freedom (DOF) force/torque sensor (ATI Nano 17, ATI Industrial Automation, Apex, NC, USA) which measures the user interaction force on the tool handle. As shown in Fig. 1, to operate SHER, both the user and the robot hold the tool and measured interaction force/torque are fed as an input into the admittance control law [9]. While the user manipulates the robot, the tool-tip interaction forces are measured using a FBG-based force sensing tool. The force sensing tool with shaft diameter of under 900 μm has a manually actuated mechanism to deploy a prebent needle (ϕ = 110 μm) [15]. Three optical fibers (Technica S.A, Beijing, China), each with one FBG sensor, are attached along the tool shaft for measuring the tool-tissue interaction force at the tool tip. As FBGs are susceptible to temperature changes, the common mode of the FBGs are used to compensate for changes in ambient temperature. Fig.2 shows the mechanical design of the tool including the syringe to pump air through the microneedle; the tool is compact and can be easily attached/detached from the robot handle. Each newly built tool needs to be calibrated to find the linear relationship between the force and the change in reflected FBG wavelength. The tool developed for this study was calibrated using a previously developed calibration method [14] and the root mean square error for force measurements was 0.7 mN.

Fig. 1.

Fig. 1.

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Experiment setup showing (a) the Steady Hand Eye Robot, combined with a force-sensing microneedle and user performing vein cannulation in a 13 days old CAM, (b) close-up view of the cannulation tool, (c) close-up view of a 13 days old CAM and (d) user holding the cannulation tool for manual operation.

Fig. 2.

Fig. 2.

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Top: force sensing cannulation tool with manually actuated microneedle, optical fibers with FBGs attached on the tool shaft for measuring tool tip force and air syringe attached to it for injecting the air to confirm whether the cannulation was successful or not. Bottom: magnified view of the microneedle.

III. Experiments

Experiments using the CAM of 13 days fertilized chicken embryos are performed to compare the robot assisted and manual vein cannulations. The experimental setup for both the modes is shown in Fig. 1. For each of the modes, total 12 cannulation attempts were performed by a surgeon operator with moderate prior cannulation experience and training on the robotic system. For both the manual and robot assisted cannulation attempts, we recorded target vein diameter, puncture force, force during the hold duration and success/failure at the time of puncture and at the end of the hold period.

A. Robot Assisted Cannulation

Fig. 1 shows the experimental setup for the robot assisted vein cannulation. The following steps were performed for each of the cannulation attempts.

  1. Prepare the egg shells to gain access to the CAM surface.

  2. Wet the egg-shell membrane with saline to improve visualization of the CAM.

  3. Stabilize the egg under the microscope as shown in Fig. 1.

  4. Identify a desired vein with an outer diameter between 200–500 μm.

  5. Operate the robot to position the microneedle on the identified target vein (Fig. 3a).

  6. Reinitialize the the FBG force sensors to zero.

  7. Perform needle insertion into the selected vein and stop the motion once needle tip is inside the lumen (Fig. 3b).

  8. Inject the air to confirm intraluminal cannulation (Fig. 3c).

  9. Hold the needle inside the vein for 30–45 seconds.

  10. Again, inject the air to confirm that the needle remains inside the lumen.

  11. Retract the needle from the vein.

  12. Observe resulting hemorrhage.

Fig. 3.

Fig. 3.

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Cannulation phases: (a) position the microneedle above the target vein, (b) perform needle insertion and hold, (c) inject air to confirm cannulation success/failure and (d) retract needle and observe hemorrhage.

The user was asked to target vein with varying sizes. To identify the size of the vein, for each cannulation attempts the needle was positioned just above the vein and an image was captured as shown in Fig. 3a to compare the vein diameter relative to the known diameter of the microneedle (110 μm). Fig. 1a and 1b show the experimental setup for the robot assisted cannulation attempts. For the manual attempts, the setup as well as the cannulation steps were similar to the robot assisted ones, except that the tool was positioned and moved manually by the surgeon.

The goal of both set of experiments was to cannulate the selected target vein on the CAM and maintain the needle inside the lumen for approximately 30–45 seconds. The user selected veins based on an approximate vein diameter of 200–500 μm. The user observed the needle insertion through the surgical microscope as shown in Fig. 1 and stopped the needle motion when he/she observed that the needle had punctured the vein. In robot assisted cannulations, while the user was holding the tool, he/she released the foot pedal to stop the robot motion to keep the microneedle inside of the vein. During the manual cannulations, the user had to constantly hold the tool while maintaining the microneedle inside of the vein. As soon as the user gave a signal that he/she was stopping the motion and believed that the needle had punctured the vein, another operator manually actuated the syringe to inject the air. Intraluminal air bubbles determined whether cannulation was successful as shown in Fig 3c. If the needle penetrated both the front and back walls of the vein, or did not penetrate the vein at all, then extra-luminal air bubbles were seen. If the cannulation was successful, the user then held the needle in place for approximately 30–45 seconds. At the end of the hold period air was again injected to confirm maintenance of the intraluminal position. Throughout this entire cannulation process, tool-tissue interaction forces were recorded at 200 Hz. The measured vein diameter, puncture force and hold force were analyzed for both the manual and robot assisted cannulation modes. Also, the statistical significance of the results were evaluated using a two-sample t-test assuming unequal variance.

IV. Results

Tool-tissue interaction forces were recorded for a total of 12 attempts for both the robot assisted and manual cannulations. Fig. 4 shows a typical force signature for robot assisted (a) and manual (b) attempts. Fig. 4a demonstrates that the drop in forces following vein puncture is more subtle and the variation in forces during the hold period is also smaller during robot assisted cannulation. Fig. 5 shows the average force during the hold period. The hold force does not depend on vein size for robot assisted cannulation, while for the manual attempts it is observed to be larger for bigger veins. A potential explanation for larger hold forces in larger veins during manual attempts may be attributed to higher vein stiffness and tension that is made more evident with hand tremor. Fig. 6 also demonstrates the impact of hand tremor, as the hold force variation is smaller for robot assisted vs. manual cannulations. Fig. 7b shows that the average puncture force for robot assisted cannulation is significantly smaller (p=0.016) than with manual attempts. A potential explanation for smaller puncture forces with robot assistance may be better control over tool motion, and the ability to perform cannulation at lower velocity. Lastly, as shown in Fig. 7d, the standard deviation of the hold force is statistically larger (p<0.001) for manual cannulations procedures as compared to robot assisted ones.

Fig. 4.

Fig. 4.

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Tip force profiles recorded during (a) robot assisted, and (b) manual vein cannulation attempts. Showing three phases of cannulation process (1) needle insertion (cyan), (2) hold the needle in-place and inject air (yellow), and (3) retract the needle (red). Also showing the start and end of hold period observed from the recorded videos.

Fig. 5.

Fig. 5.

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Average holding force: comparison of targeted vein diameter vs. the tool-tip force during the hold period for manual and robot assisted vein cannulations.

Fig. 6.

Fig. 6.

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Holding force range: comparison of targeted vein diameter vs. the tool-tip force range during the hold period for manual and robot assisted vein cannulations.

Fig. 7.

Fig. 7.

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Comparison of robot assisted and manual attempts from left to right: (a) targeted vein diameter, (b) puncture force, (c) average force during hold period and (d) standard deviation of measured force during hold duration.

V. Discussion and Conclusions

Retinal vein cannulation demands precise microneedle manipulation for successful intraluminal cannulation and the position maintenance necessary for injectinon of anticoagulating agents over a period of 30 to 120 seconds. Surgeon hand tremor on the order of 100 μm makes it challenging to consistently and safely cannulate a 200 μm diameter vein with a 110 μm diameter needle. A cooperatively controlled robotic assistant with tremor cancellation capabilities and precise maneuverability may mitigate some of the challenges of retinal vein cannulation. An FBG-based force sensing microneedle tool may provide further insights into relevant tool-tissue interactions during cannulation. In this paper, we present a comparison of manual and robot assisted vein cannulations in the CAM of 13 days old chicken embryos. A clinician with a moderate experience in operating the robot as well as cannulation performed 12 needle insertions for each of the operating modes. For each insertion, he/she was asked to hold the needle inside the vein lumen for approximately 30–40 seconds. During the entire cannulation process, we recorded tool-tissue interaction forces. Vein diameter for all the cannulation attempts was 220–410 μm. We observed that the average puncture force for robot assisted cannulations (13.00 mN) was statistically (p=0.016) smaller than those measured during manual attempts (17.71 mN). Though, the average hold force was similar for both modes, the variability in forces during the intraluminal “hold period” was also significantly (p<0.001) larger for manual attempts as compared to robot assisted ones; this strongly suggests that robot assistance is helpful in maintaining an intraluminal needle position in small vessels, without exerting additional force.

Retinal veins of interest in a clinically relevant scenario are often smaller than 200 μm in diameter. Though, in this work we did not exclusively target such small veins, our goal was to compare the tool-tissue interactions in manual and robot assisted cannulations. Also, in the presented work, though the chicken eggs were fixed on a support, the CAM and the veins were not stabilized and had natural elasticity and motion related to blood pressure changes during the cardiac cycle. Though, the veins in the retina are more fixated than the CAM veins, applying any external fixation to CAM veins could result in additional forces. To better emulate the surgical environment, we plan to conduct similar studies in a rabbit model. In a living eye model, there may be additional venous movements to contend with such as those resulting from respiration and head movements. In future work, we will implement vein puncture detection to optimize successful cannulation. Also, we will adopt a motion compensation strategy presented in [17] to detect vein motion using the observed tool-tip force changes and utilize robotic compensation.

ACKNOWLEDGEMENTS

This work was supported by U.S. National Institutes of Health under grant number 1R01EB023943-01. The work of M. Urias was also supported by Instituto da Visão (IPEPO) and Lemann Foundation. The work of PLG was supported in part by Research to Prevent Blindness, New York, USA, and gifts by the J. Willard and Alice S. Marriott Foundation, the Gale Trust, Mr. Herb Ehlers, Mr. Bill Wilbur, Mr. and Mrs. Rajandre Shaw, Ms. Helen Nassif, Ms Mary Ellen Keck, and Mr. Ronald Stiff.

Contributor Information

Niravkumar Patel, Laboratory for Computational Sensing and Robotics, Johns Hopkins University, 3400 N. Charles Street, Baltimore, MD USA-21218.

Muller Urias, Wilmer Eye Institute at the Johns Hopkins Hospital, Baltimore, MD 21287 USA; Federal University of São Paulo, São Paulo, 04023-062 Brazil.

Changyan He, Laboratory for Computational Sensing and Robotics, Johns Hopkins University, 3400 N. Charles Street, Baltimore, MD USA-21218; School of Mechanical Engineering and Automation, Beihang University, Beijing, China, 100191.

Peter L. Gehlbach, Wilmer Eye Institute at the Johns Hopkins Hospital, Baltimore, MD 21287 USA

Iulian Iordachita, Laboratory for Computational Sensing and Robotics, Johns Hopkins University, 3400 N. Charles Street, Baltimore, MD USA-21218.

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