Motorized Micro-Forceps with Active Motion Guidance based on Common-Path SSOCT for Epiretinal Membranectomy

Microsurgery Robots: Applications, Design, and Development

Microsurgical techniques have been widely utilized in various surgical specialties, such as ophthalmology, neurosurgery, and otolaryngology, which require intricate and precise surgical tool manipulation on a small scale. In microsurgery, operations on delicate vessels or tissues require high standards in surgeons' skills. This exceptionally high requirement in skills leads to a steep learning curve and lengthy training before the surgeons can perform microsurgical procedures with quality outcomes. The microsurgery robot (MSR), which can improve surgeons' operation skills through various functions, has received extensive research attention in the past three decades. There have been many review papers summarizing the research on MSR for specific surgical specialties. However, an in-depth review of the relevant technologies used in MSR systems is limited in the literature. This review details the technical challenges in microsurgery, and systematically summarizes the key technologies in MSR with a developmental perspective from the basic structural mechanism design, to the perception and human-machine interaction methods, and further to the ability in achieving a certain level of autonomy. By presenting and comparing the methods and technologies in this cutting-edge research, this paper aims to provide readers with a comprehensive understanding of the current state of MSR research and identify potential directions for future development in MSR.

Simultaneous Online Registration-Independent Stiffness Identification and Tip Localization of Surgical Instruments in Robot-assisted Eye Surgery

Notable challenges during retinal surgery lend themselves to robotic assistance which has proven beneficial in providing a safe steady-hand manipulation. Efficient assistance from the robots heavily relies on accurate sensing of surgery states (e.g. instrument tip localization and tool-to-tissue interaction forces). Many of the existing tool tip localization methods require preoperative frame registrations or instrument calibrations. In this study using an iterative approach and by combining vision and force-based methods, we develop calibration- and registration-independent (RI) algorithms to provide online estimates of instrument stiffness (least squares and adaptive). The estimations are then combined with a state-space model based on the forward kinematics (FWK) of the Steady-Hand Eye Robot (SHER) and Fiber Brag Grating (FBG) sensor measurements. This is accomplished using a Kalman Filtering (KF) approach to improve the deflected instrument tip position estimations during robot-assisted eye surgery. The conducted experiments demonstrate that when the online RI stiffness estimations are used, the instrument tip localization results surpass those obtained from pre-operative offline calibrations for stiffness.

Convolutional neural network-based common-path optical coherence tomography A-scan boundary-tracking training and validation using a parallel Monte Carlo synthetic dataset

We present a parallel Monte Carlo (MC) simulation platform for rapidly generating synthetic common-path optical coherence tomography (CP-OCT) A-scan image dataset for image-guided needle insertion. The computation time of the method has been evaluated on different configurations and 100000 A-scan images are generated based on 50 different eye models. The synthetic dataset is used to train an end-to-end convolutional neural network (Ascan-Net) to localize the Descemet's membrane (DM) during the needle insertion. The trained Ascan-Net has been tested on the A-scan images collected from the ex-vivo human and porcine cornea as well as simulated data and shows improved tracking accuracy compared to the result by using the Canny-edge detector.

Higher-order regression three-dimensional motion-compensation method for real-time optical coherence tomography volumetric imaging of the cornea

SIGNIFICANCE

Optical coherence tomography (OCT) allows high-resolution volumetric three-dimensional (3D) imaging of biological tissues in vivo. However, 3D-image acquisition can be time-consuming and often suffers from motion artifacts due to involuntary and physiological movements of the tissue, limiting the reproducibility of quantitative measurements.

AIM

To achieve real-time 3D motion compensation for corneal tissue with high accuracy.

APPROACH

We propose an OCT system for volumetric imaging of the cornea, capable of compensating both axial and lateral motion with micron-scale accuracy and millisecond-scale time consumption based on higher-order regression. Specifically, the system first scans three reference B-mode images along the C-axis before acquiring a standard C-mode image. The difference between the reference and volumetric images is compared using a surface-detection algorithm and higher-order polynomials to deduce 3D motion and remove motion-related artifacts.

RESULTS

System parameters are optimized, and performance is evaluated using both phantom and corneal (ex vivo) samples. An overall motion-artifact error of <4.61 microns and processing time of about 3.40 ms for each B-scan was achieved.

CONCLUSIONS

Higher-order regression achieved effective and real-time compensation of 3D motion artifacts during corneal imaging. The approach can be expanded to 3D imaging of other ocular tissues. Implementing such motion-compensation strategies has the potential to improve the reliability of objective and quantitative information that can be extracted from volumetric OCT measurements.

Adaptive Control Improves Sclera Force Safety in Robot-Assisted Eye Surgery: A Clinical Study

The integration of robotics into retinal microsurgery leads to a reduction in surgeon perception of tool-to-tissue interaction forces. This blunting of human tactile sensory input, which is due to the inflexible mass and large inertia of the robotic arm as compared to the milli-Newton scale of the interaction forces and fragile tissues during ophthalmic surgery, identifies a potential iatrogenic risk during robotic eye surgery. In this paper, we aim to evaluate two variants of an adaptive force control scheme implemented on the Steady-Hand Eye Robot (SHER) that are intended to mitigate the risk of unsafe scleral forces. The present study enrolled ten retina fellows and ophthalmology residents into a simulated procedure, which simply asked the trainees to follow retinal vessels in a model retina surgery environment. For this purpose, we have developed a force-sensing (equipped with Fiber Bragg Grating (FBG)) instrument to attach to the robot. A piezo-actuated linear stage for creating random lateral motions to the eyeball phantom has been provided to simulate disturbances during surgery. The SHER and all of its dependencies were set up in an operating room in the Wilmer Eye Institute at the Johns Hopkins Hospital. The clinicians conducted robot-assisted experiments with the adaptive controls incorporated as well as freehand manipulations. The results indicate that the Adaptive Norm Control (ANC) method, is able to maintain scleral forces at predetermined safe levels better than even freehand manipulations. Novice clinicians in robot training however, subjectively preferred freehand maneuvers over robotic manipulations. Clinician preferences once highly skilled with the robot is not assessed in this study.

CNN-based CP-OCT sensor integrated with a subretinal injector for retinal boundary tracking and injection guidance

SIGNIFICANCE

Subretinal injection is an effective way of delivering transplant genes and cells to treat many degenerative retinal diseases. However, the technique requires high-dexterity and microscale precision of experienced surgeons, who have to overcome the physiological hand tremor and limited visualization of the subretinal space.

AIM

To automatically guide the axial motion of microsurgical tools (i.e., a subretinal injector) with microscale precision in real time using a fiber-optic common-path swept-source optical coherence tomography distal sensor.

APPROACH

We propose, implement, and study real-time retinal boundary tracking of A-scan optical coherence tomography (OCT) images using a convolutional neural network (CNN) for automatic depth targeting of a selected retinal boundary for accurate subretinal injection guidance. A simplified 1D U-net is used for the retinal layer segmentation on A-scan OCT images. A Kalman filter, combining retinal boundary position measurement by CNN and velocity measurement by cross correlation between consecutive A-scan images, is applied to optimally estimate the retinal boundary position. Unwanted axial motions of the surgical tools are compensated by a piezoelectric linear motor based on the retinal boundary tracking.

RESULTS

CNN-based segmentation on A-scan OCT images achieves the mean unsigned error (MUE) of ∼3  pixels (8.1  μm) using an ex vivo bovine retina model. GPU parallel computing allows real-time inference (∼2  ms) and thus real-time retinal boundary tracking. Involuntary tremors, which include low-frequency draft in hundreds of micrometers and physiological tremors in tens of micrometers, are compensated effectively. The standard deviations of photoreceptor (PR) and choroid (CH) boundary positions get as low as 10.8  μm when the depth targeting is activated.

CONCLUSIONS

A CNN-based common-path OCT distal sensor successfully tracks retinal boundaries, especially the PR/CH boundary for subretinal injection, and automatically guides the tooltip's axial position in real time. The microscale depth targeting accuracy of our system shows its promising possibility for clinical application.

Stochastic Force-based Insertion Depth and Tip Position Estimations of Flexible FBG-Equipped Instruments in Robotic Retinal Surgery

Vitreoretinal surgery is among the most delicate surgical tasks during which surgeon hand tremor may severely attenuate surgeon performance. Robotic assistance has been demonstrated to be beneficial in diminishing hand tremor. Among the requirements for reliable assistance from the robot is to provide precise measurements of system states e.g. sclera forces, tool tip position and tool insertion depth. Providing this and other sensing information using existing technology would contribute towards development and implementation of autonomous robot-assisted tasks in retinal surgery such as laser ablation, guided suture placement/assisted needle vessel cannulation, among other applications. In the present work, we use a state-estimating Kalman filtering (KF) to improve the tool tip position and insertion depth estimates, which used to be purely obtained by robot forward kinematics (FWK) and direct sensor measurements, respectively. To improve tool tip localization, in addition to robot FWK, we also use sclera force measurements along with beam theory to account for tool deflection. For insertion depth, the robot FWK is combined with sensor measurements for the cases where sensor measurements are not reliable enough. The improved tool tip position and insertion depth measurements are validated using a stereo camera system through preliminary experiments and a case study. The results indicate that the tool tip position and insertion depth measurements are significantly improved by 77% and 94% after applying KF, respectively.

Semiautomated optical coherence tomography-guided robotic surgery for porcine lens removal

PURPOSE

To evaluate semiautomated surgical lens extraction procedures using the optical coherence tomography (OCT)-integrated Intraocular Robotic Interventional Surgical System.

SETTING

Stein Eye Institute and Department of Mechanical and Aerospace Engineering, University of California, Los Angeles, USA.

DESIGN

Experimental study.

METHODS

Semiautomated lens extraction was performed on postmortem pig eyes using a robotic platform integrated with an OCT imaging system. Lens extraction was performed using a series of automated steps including robot-to-eye alignment, irrigation/aspiration (I/A) handpiece insertion, anatomic modeling, surgical path planning, and I/A handpiece navigation. Intraoperative surgical supervision and human intervention were enabled by real-time OCT image feedback to the surgeon via a graphical user interface. Manual preparation of the pig-eye models, including the corneal incision and capsulorhexis, was performed by a trained cataract surgeon before the semiautomated lens extraction procedures. A scoring system was used to assess surgical complications in a postoperative evaluation.

RESULTS

Complete lens extraction was achieved in 25 of 30 eyes. In the remaining 5 eyes, small lens pieces (≤1.0 mm³) were detected near the lens equator, where transpupillary OCT could not image. No posterior capsule rupture or corneal leakage occurred. The mean surgical duration was 277 seconds ± 42 (SD). Based on a 3-point scale (0 = no damage), damage to the iris was 0.33 ± 0.20, damage to the cornea was 1.47 ± 0.20 (due to tissue dehydration), and stress at the incision was 0.97 ± 0.11.

CONCLUSIONS

No posterior capsule rupture was reported. Complete lens removal was achieved in 25 trials without significant surgical complications. Refinements to the procedures are required before fully automated lens extraction can be realized.

Optical Coherence Tomography Guided Robotic Needle Insertion for Deep Anterior Lamellar Keratoplasty

OBJECTIVE

Deep anterior lamellar keratoplasty (DALK) significantly reduces the post-transplantation morbidity in patients eligible for partial-thickness cornea grafts. The popular "big bubble" technique for DALK is so challenging, however, that a significant fraction of corneal pneumodissection attempts fail for surgeons without extensive DALK-specific experience, even with previous-generation cross-sectional optical coherence tomography (OCT) guidance. We seek to develop robotic, volumetric OCT-guided technology capable of facilitating or automating the difficult needle insertion step in DALK.

METHODS

Our system provides for real-time volumetric corneal imaging, segmentation, and tracking of the needle insertion to display feedback for surgeons and to generate needle insertion plans for robotic execution. We include a non-automatic mode for cooperative needle control for stabilization and tremor attenuation, and an automatic mode in which needle insertion plans are generated based on OCT tracking results and executed under surgeon hold-to-run control by the robot arm. We evaluated and compared freehand, volumetric OCT-guided, cooperative, and automatic needle insertion approaches in terms of perforation rate and final needle depth in an ex vivo human cornea model.

RESULTS

Volumetric OCT visualization reduces cornea perforations and beneficially increases final needle depth in manual insertions by clinically significant amounts. Our automatic robotic needle insertion techniques meet or exceed surgeon performance in both needle placement and perforation rate.

CONCLUSION

Volumetric OCT is a key enabler for surgeons, although robotic techniques can reliably replicate their performance.

SIGNIFICANCE

Robotic needle control and volumetric OCT promise to improve outcomes in DALK.

Intraocular robotic interventional surgical system (IRISS): Semi-automated OCT-guided cataract removal

BACKGROUND

With the development of laser-assisted platforms, the outcomes of cataract surgery have been improved by automating several procedures. The cataract-extraction step continues to be manually performed, but due to deficiencies in sensing capabilities, surgical complications such as posterior capsule rupture and incomplete cataract removal remain.

METHODS

An optical coherence tomography (OCT) system is integrated into our intraocular robotic interventional surgical system (IRISS) robot. The OCT images are used for preoperative planning and intraoperative intervention in a series of automated procedures. Real-time intervention allows surgeons to evaluate the progress and override the operation.

RESULTS

The developed system was validated by performing lens extraction on 30 postmortem pig eyes. Complete lens extraction was achieved on 25 eyes, and "almost complete" extraction was achieved on the remainder due to an inability to image small lens particles behind the iris. No capsule rupture was found.

CONCLUSION

The IRISS successfully demonstrated semiautomated OCT-guided lens removal with real-time supervision and intervention.