Abstract

Retinal vein cannulation is a technically demanding surgical procedure where therapeutic agents are injected into the retinal veins to treat occlusions. The clinical feasibility of this approach has been largely limited by the technical challenges associated with performing the procedure. Among the challenges to successful vein cannulation are identifying the moment of venous puncture, achieving cannulation of the micro-vessel, and maintaining cannulation throughout drug delivery. Recent advances in medical robotics and sensing of tool-tissue interaction forces have the potential to address each of these challenges as well as to prevent tissue trauma, minimize complications, diminish surgeon effort, and ultimately promote successful retinal vein cannulation. In this paper, we develop an assistive system combining a handheld micromanipulator, called “Micron”, with a force-sensing microneedle. Using this system, we examine two distinct methods of precisely detecting the instant of venous puncture. This is based on measured tool-tissue interaction forces and also the tracked position of the needle tip. In addition to the existing tremor canceling function of Micron, a new control method is implemented to actively compensate unintended movements of the operator, and to keep the cannulation device securely inside the vein following cannulation. To demonstrate the capabilities and performance of our uniquely upgraded system, we present a multi-user artificial phantom study with subjects from three different surgical skill levels. Results show that our puncture detection algorithm, when combined with the active positive holding feature enables sustained cannulation which is most evident in smaller veins. Notable is that the active holding function significantly attenuates tool motion in the vein, thereby reduces the trauma during cannulation.

Highlights

  • To prevent significant performance degradation in time due to fatigue, tests were completed in three periods, each period involving a total of eight trials, with a 10-min break between the periods

  • Retinal vein vein cannulation cannulation is is aa demanding demanding procedure, procedure, and and its its feasibility feasibility is is currently currently limited limited by by the the Retinal challenges in in identifying identifying the the moment moment of of venous puncture, achieving achieving cannulation cannulation and and maintaining maintaining challenges cannulation during during drug drug delivery. Study, we addressed these these problems problems using using aa force-sensing force-sensing cannulation microneedle combined a handheld roboticrobotic micromanipulator, leading toleading two maintocontributions

  • The first method based on the time derivative of only the tool tip forces required careful of and investigated

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Summary

Motivation

Retinal vein occlusion (RVO) is one of the most common retinal vascular diseases affecting approximately 16.4 million people worldwide [1], with a prevalence of 1.8% and 0.5% for central and branch retinal veins, respectively [2]. The procedure involves three main steps: (1) accurately bringing a sharp tipped cannula onto the occluded retinal vein, (2) puncturing through the vein wall and precisely halting the cannula tip at the right depth, and (3) advancing and maintaining the cannula inside the vein for several minutes, during which a therapeutic agent e.g., tissue plasminogen activator (t-PA) [11] or ocriplasmin [12] or other is delivered to dissolve the thrombus This is a very demanding and risky procedure because of the small size and fragility of retinal veins—especially if the occlusion is in a branch retinal vein (typically Ø < 200 μm) rather than the central vein [13]

Background
Actuation Mechanism
Motorized
Force Sensor Integration
Integration with a Handheld Micromanipulation System
Sample handheld
Control
Force-Based Method
Force-and-Position-Based Method
4.4.Experiments
Experimental
Evaluation of the Puncture Detection Methods
10. Measurements
Evaluation
Experiment 2: A Multi-User System Evaluation
14. Deviation
Conclusions
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