This paper describes a novel tissue positioning system with an integrated suturing robot and demonstrates its ability to perform semi-automatic anastomoses of synthetic blood vessels. We began with a finite element analysis-based design consideration for achieving adequate grasping of blood vessels to demonstrate robust performance under expected clinical forces. We then conducted standardized positioning tests to measure the repeatability of the system and incorporated a high-resolution optical coherence tomography (OCT) fiber imaging sensor within the tip of the suturing tool to provide position feedback of the robot during a suturing task. Using the microvascular positioner and OCT sensor, the system performed semi-automatic suturing of synthetic 5 mm diameter blood vessels ($\mathrm{N}=4$), and the suture quality was evaluated for consistency in spacing, bite depth, percent lumen reduction, and maximum suture strength. The system completed the task in an average time of 31.75 minutes. The samples had zero missed stitches, average spacing of 1.64 mm, an average bite depth of 2.14 mm, an average lumen reduction of 57.98%, and an average suture strength of 3.13 N.

This article introduces a novel suture managing device (SMD) and new suture management controller to enable single-arm suture management during autonomous suturing with the Smart Tissue Autonomous Robot (STAR). The primary function of the SMD is to tension and manage the suture thread, a task that was previously carried out by a second manipulator or a human assistant. The SMD and its controller are integrated into STAR's autonomous suturing workflow. Experiments were conducted to quantify the tensioning force of SMD and to evaluate the suture quality of the new single-arm system. The prototype of SMD achieves 1.67N tensioning force with suturing time of 29.1±0.42 seconds per stitch. Our study results demonstrate that the single-arm STAR system with SMD achieves equivalent performance to our previous works in suturing efficiency where suture management was performed with either a dual-armed robotic system or by a human surgical assistant. The study's findings contribute to the field of medical robotics and to our knowledge represent the first known instance of single-arm suturing with suture management during autonomous anastomosis.

Subretinal injection (SI) is an ophthalmic surgical procedure that allows for the direct injection of therapeutic substances into the subretinal space to treat vitreoretinal disorders. Although this treatment has grown in popularity, various factors contribute to its difficulty. These include the retina’s fragile, nonregenerative tissue, as well as hand tremor and poor visual depth perception. In this context, the usage of robotic devices may reduce hand tremors and facilitate gradual and controlled SI. For the robot to successfully move to the target area, it needs to understand the spatial relationship between the attached needle and the tissue. The development of optical coherence tomography (OCT) imaging has resulted in a substantial advancement in visualizing retinal structures at micron resolution. This paper introduces a novel foundation for an OCT-guided robotic steering framework that enables a surgeon to plan and select targets within the OCT volume. At the same time, the robot automatically executes the trajectories necessary to achieve the selected targets. Our contribution consists of a novel combination of existing methods, creating an intraoperative OCT-Robot registration pipeline. We combined straightforward affine transformation computations with robot kinematics and a deep neural network-determined tool-tip location in OCT. We evaluate our framework’s capability in a cadaveric pig eye open-sky procedure and using an aluminum target board. Targeting the subretinal space of the pig eye produced encouraging results with a mean Euclidean error of 23.8μm.

Fast and robust small infrared (IR) target detection is a challenging task and critical to the performance of IR searching and tracking (IRST) systems. However, the current algorithms generally have difficulty in striking a good balance between speed and performance. In this letter, we propose a new approach to small IR target detection that can significantly accelerate the detection process by first performing a fast adaptive masking and scaling algorithm. We then propose to enhance the target characteristics and suppress the background clutter using both contrast and gradient information. Finally, we propose to accurately extract the targets via iterative segmentation. The experimental results demonstrated that our proposed method yields the best and the most robust performance, with a speed of at least two times faster than the state-of-the-art methods.

Autonomous robotic suturing has the potential to improve surgery outcomes by leveraging accuracy, repeatability, and consistency compared to manual operations. However, achieving full autonomy in complex surgical environments is not practical and human supervision is required to guarantee safety. In this paper, we develop a confidence-based supervised autonomous suturing method to perform robotic suturing tasks via both Smart Tissue Autonomous Robot (STAR) and surgeon collaboratively with the highest possible degree of autonomy. Via the proposed method, STAR performs autonomous suturing when highly confident and otherwise asks the operator for possible assistance in suture positioning adjustments. We evaluate the accuracy of our proposed control method via robotic suturing tests on synthetic vaginal cuff tissues and compare them to the results of vaginal cuff closures performed by an experienced surgeon. Our test results indicate that by using the proposed confidence-based method, STAR can predict the success of pure autonomous suture placement with an accuracy of 94.74%. Moreover, via an additional 25% human intervention, STAR can achieve a 98.1% suture placement accuracy compared to an 85.4% accuracy of completely autonomous robotic suturing. Finally, our experiment results indicate that STAR using the proposed method achieves 1.6 times better consistency in suture spacing and 1.8 times better consistency in suture bite sizes than the manual results.

This paper reports the design and evaluation of a novel piezo based actuator for needle drive in autonomous Deep Anterior Lamellar Keratoplasty (piezo-DALK). The actuator weighs less than 8g and is 20mm × 20mm × 10.5mm in size, making it ideal for eye-mounted applications. Mean open loop positional deviation was 1.17 ± 3.15um, and system repeatability and accuracy were 17.16um and 18.33um, respectively. Stall force was found to vary linearly with the cooling cycle and the actuator achieved a maximum drive force of 3.98N. When simulating the DALK procedure in synthetic corneal tissue, the piezo-DALK achieved a penetration depth of 643.56um which was equivalent to 92.1% of the total corneal thickness. This correlated closely with our desired depth of 90% ± 5% and took 2.5 hours to achieve. This work represents the first eye mountable actuator capable of "Big Bubble" needle drive for autonomous DALK procedures.

Compared to open surgical techniques, laparoscopic surgical methods aim to reduce the collateral tissue damage and hence decrease the patient recovery time. However, constraints imposed by the laparoscopic surgery, i.e. the operation of surgical tools in limited spaces, turn simple surgical tasks such as suturing into time-consuming and inconsistent tasks for surgeons. In this paper, we develop an autonomous laparoscopic robotic suturing system. More specific, we expand our smart tissue anastomosis robot (STAR) by developing i) a new 3D imaging endoscope, ii) a novel actuated laparoscopic suturing tool, and iii) a suture planning strategy for the autonomous suturing. We experimentally test the accuracy and consistency of our developed system and compare it to sutures performed manually by surgeons. Our test results on suture pads indicate that STAR can reach 2.9 times better consistency in suture spacing compared to manual method and also eliminate suture repositioning and adjustments. Moreover, the consistency of suture bite sizes obtained by STAR matches with those obtained by manual suturing.

This paper reports the design and evaluation of a novel robotic device for cornea transplant surgery. The device enables the OCT-sensor guided Big Bubble hydro-dissection approach for deep anterior lamellar keratoplasty (DALK) cornea transplant surgery. DALK is highly challenging because it requires precise placement of a needle into the stroma of the cornea down to Descemets Membrane (DM) and injects a fluid to separate the remaining stroma from Descemet's membrane. Finally, the stroma is removed and replaced with the donor cornea graft. Compared to traditional penetrating keratoplasty (PK), which involves a full-thickness graft, this method significantly reduces the risk of rejection of the donor cornea by keeping the DM intact. A comparison of autonomous OCT guided needle insertions with expert manual needle insertions showed that the device significantly increased the precision and consistency of the needle placement, which could lead to better visual outcomes and fewer complications. In a study on cadaver porcine eyes, the measured insertion depth as a percentage of cornea thickness for the robotic device was 90.05% +/- 2.33% compared to 79.16% +/- 5.68% for manual insertions.

This paper reports a robotic laparoscopic surgery system performing electro-surgery on porcine cadaver kidney, and evaluates its accuracy in an open loop control scheme to conduct targeting and cutting tasks guided by a novel 3D endoscope. We describe the design and integration of the novel laparoscopic imaging system that is capable of reconstructing the surgical field using structured light. A targeting task is first performed to determine the average positioning error of the system as guided by the laparoscopic camera. The imaging system is then used to reconstruct the surface of a porcine cadaver kidney, and generate a cutting trajectory with consistent depth. The paper concludes by using the robotic system in open loop control to cut this trajectory using a multi degree of freedom electro-surgical tool. It is demonstrated that for a cutting depth of 3 mm, the robotic surgical system follows the trajectory with an average depth of 2.44 mm and standard deviation of 0.34 mm. The average positional accuracy of the system was 2.74±0.99 mm.

Biopsy is a medical procedure involving the surgical removal of tissue for close examination. As a rule, a biopsy is performed on suspected cancerous tissue in order to determine the appearance of cancer cells and the cancer stage. Obtaining accurate evaluation of the suspected tissue requires high-resolution optical imaging that allows a clear view of the cell structure. This requires chemical treatment in some cases, freezing, dying, thinly slicing, and examining the tissue under a high-magnification microscope. The downside of the biopsy is that it is an invasive surgical procedure, there could be serious surgical complications such as bleeding and/or infection, and it can take several days to weeks before the final pathological report is ready to be presented to the patient. These limitations have motivated researchers to develop ways to achieve a high-resolution internal cellular imaging of the cancerous tissue noninvasively and in real time. From the advent of noninvasive three-dimensional (3D) imaging techniques such as ultrasound and magnetic resonance imaging (MRI) for more than 50 and 30 years, respectively, there have been extensive efforts by many researchers to attain "virtual biopsy." Some techniques have been shown and proven to be highly effective diagnostic tools for different cancer types. However, none is being effectively developed as a screening tool that can replace traditional biopsy. Consequently, many people in the field are asking and wondering if virtual biopsy is a realistic screening tool for the future. A proper screening of the cancerous tissue requires highly magnified images of an internal cellular structure. In order to perform subsurface imaging, 3D imaging modalities such as computed tomography (CT) scan, MRI, ultrasound, optical coherence tomography (OCT), and confocal microscope could be used and could be candidates for "virtual biopsy." This paper will discuss some of the proposed techniques for virtual biopsy.

This paper reports the development of novel micro-force sensing tools for retinal microsurgery. Retinal microsurgery requires extremely delicate manipulation of retinal tissue, and tool-to-tissue interaction forces are frequently below human perceptual thresholds. Further, the interaction between the tool shaft and sclera makes accurate sensing of forces exerted on the retina very difficult with previously developed force sensing schemes, in which the sensor is located outside the eye. In the work reported here, we incorporate 160 µm Fiber Bragg Grating (FBG) strain sensors into the tool shaft to sense forces distal to the sclera. The sensor is applicable both with robotically manipulated and freehand tools. Preliminary results with a 1 degree-of-freedom (DOF) sensor have demonstrated 0.25 mN resolution, and work is underway to develop 2 and 3 DOF tools. The design and analysis of the force sensing tool is presented with preliminary testing data and some initial experiments using the tool with both freehand and robotic manipulation.