Patient-specific tissue engineered vascular graft for aortic arch reconstruction.

Objectives: The complexity of aortic arch reconstruction due to diverse 3-dimensional geometrical abnormalities is a major challenge. This study introduces 3-dimensional printed tissue-engineered vascular grafts, which can fit patient-specific dimensions, optimize hemodynamics, exhibit antithrombotic and anti-infective properties, and accommodate growth. Methods: We procured cardiac magnetic resonance imaging with 4-dimensional flow for native porcine anatomy (n = 10), from which we designed tissue-engineered vascular grafts for the distal aortic arch, 4 weeks before surgery. An optimal shape of the curved vascular graft was designed using computer-aided design informed by computational fluid dynamics analysis. Grafts were manufactured and implanted into the distal aortic arch of porcine models, and postoperative cardiac magnetic resonance imaging data were collected. Pre- and postimplant hemodynamic data and histology were analyzed. Results: Postoperative magnetic resonance imaging of all pigs with 1:1 ratio of polycaprolactone and poly-L-lactide-co-ε-caprolactone demonstrated no specific dilatation or stenosis of the graft, revealing a positive growth trend in the graft area from the day after surgery to 3 months later, with maintaining a similar shape. The peak wall shear stress of the polycaprolactone/poly-L-lactide-co-ε-caprolactone graft portion did not change significantly between the day after surgery and 3 months later. Immunohistochemistry showed endothelization and smooth muscle layer formation without calcification of the polycaprolactone/poly-L-lactide-co-ε-caprolactone graft. Conclusions: Our patient-specific polycaprolactone/poly-L-lactide-co-ε-caprolactone tissue-engineered vascular grafts demonstrated optimal anatomical fit maintaining ideal hemodynamics and neotissue formation in a porcine model. This study provides a proof of concept of patient-specific tissue-engineered vascular grafts for aortic arch reconstruction.

Automatic and real-time tissue sensing for autonomous intestinal anastomosis using hybrid MLP-DC-CNN classifier-based optical coherence tomography.

Anastomosis is a common and critical part of reconstructive procedures within gastrointestinal, urologic, and gynecologic surgery. The use of autonomous surgical robots such as the smart tissue autonomous robot (STAR) system demonstrates an improved efficiency and consistency of the laparoscopic small bowel anastomosis over the current da Vinci surgical system. However, the STAR workflow requires auxiliary manual monitoring during the suturing procedure to avoid missed or wrong stitches. To eliminate this monitoring task from the operators, we integrated an optical coherence tomography (OCT) fiber sensor with the suture tool and developed an automatic tissue classification algorithm for detecting missed or wrong stitches in real time. The classification results were updated and sent to the control loop of STAR robot in real time. The suture tool was guided to approach the object by a dual-camera system. If the tissue inside the tool jaw was inconsistent with the desired suture pattern, a warning message would be generated. The proposed hybrid multilayer perceptron dual-channel convolutional neural network (MLP-DC-CNN) classification platform can automatically classify eight different abdominal tissue types that require different suture strategies for anastomosis. In MLP, numerous handcrafted features (∼1955) were utilized including optical properties and morphological features of one-dimensional (1D) OCT A-line signals. In DC-CNN, intensity-based features and depth-resolved tissues' attenuation coefficients were fully exploited. A decision fusion technique was applied to leverage the information collected from both classifiers to further increase the accuracy. The algorithm was evaluated on 69,773 testing A-line data. The results showed that our model can classify the 1D OCT signals of small bowels in real time with an accuracy of 90.06%, a precision of 88.34%, and a sensitivity of 87.29%, respectively. The refresh rate of the displayed A-line signals was set as 300 Hz, the maximum sensing depth of the fiber was 3.6 mm, and the running time of the image processing algorithm was ∼1.56 s for 1,024 A-lines. The proposed fully automated tissue sensing model outperformed the single classifier of CNN, MLP, or SVM with optimized architectures, showing the complementarity of different feature sets and network architectures in classifying intestinal OCT A-line signals. It can potentially reduce the manual involvement of robotic laparoscopic surgery, which is a crucial step towards a fully autonomous STAR system.

Autonomous System for Tumor Resection (ASTR) - Dual-Arm Robotic Midline Partial Glossectomy.

Head and neck cancers are the seventh most common cancers worldwide, with squamous cell carcinoma being the most prevalent histologic subtype. Surgical resection is a primary treatment modality for many patients with head and neck squamous cell carcinoma, and accurately identifying tumor boundaries and ensuring sufficient resection margins are critical for optimizing oncologic outcomes. This study presents an innovative autonomous system for tumor resection (ASTR) and conducts a feasibility study by performing supervised autonomous midline partial glossectomy for pseudotumor with millimeter accuracy. The proposed ASTR system consists of a dual-camera vision system, an electrosurgical instrument, a newly developed vacuum grasping instrument, two 6-DOF manipulators, and a novel autonomous control system. The letter introduces an ontology-based research framework for creating and implementing a complex autonomous surgical workflow, using the glossectomy as a case study. Porcine tongue tissues are used in this study, and marked using color inks and near-infrared fluorescent (NIRF) markers to indicate the pseudotumor. ASTR actively monitors the NIRF markers and gathers spatial and color data from the samples, enabling planning and execution of robot trajectories in accordance with the proposed glossectomy workflow. The system successfully performs six consecutive supervised autonomous pseudotumor resections on porcine specimens. The average surface and depth resection errors measure 0.73±0.60 mm and 1.89±0.54 mm, respectively, with no positive tumor margins detected in any of the six resections. The resection accuracy is demonstrated to be on par with manual pseudotumor glossectomy performed by an experienced otolaryngologist.

In vitro investigation of axial mechanical support devices implanted in the novel convergent cavopulmonary connection Fontan.

OBJECTIVES: The 2 opposing inflows and 2 outflows in a total cavopulmonary connection make mechanical circulatory support (MCS) extremely challenging. We have previously reported a novel convergent cavopulmonary connection (CCPC) Fontan design that improves baseline characteristics and provides a single inflow and outflow, thus simplifying MCS. This study aims to assess the feasibility of MCS of this novel configuration using axial flow pumps in an in vitro benchtop model. METHODS: Three-dimensional segmentations of 12 single-ventricle patients (body surface area 0.5-1.75 m2) were generated from cardiovascular magnetic resonance images. The CCPC models were designed by connecting the inferior vena cava and superior vena cava to a shared conduit ascending to the pulmonary arteries, optimized in silico. The 12 total cavopulmonary connection and their corresponding CCPC models underwent in vitro benchtop characterization. Two MCS devices were used, the Impella RP® and the PediPump. RESULTS: MCS successfully and symmetrically reduced the pressure in both vena cavae by >20 mmHg. The devices improved the hepatic flow distribution balance of all CCPC models (Impella RP®P = 0.045, PediPump P = 0.055). CONCLUSIONS: The CCPC Fontan design provides a feasible MCS solution for a failing Fontan by balancing hepatic flow distribution and symmetrically decompressing the central venous pressure. Cardiac index may also improve with MCS. Additional studies are needed to evaluate this concept for managing Fontan failure.

Virtual Planning and Patient-Specific Graft Design for Aortic Repairs.

PURPOSE: Patients presenting with coarctation of the aorta (CoA) may also suffer from co-existing transverse arch hypoplasia (TAH). Depending on the risks associated with the surgery and the severity of TAH, clinicians may decide to repair only CoA, and monitor the TAH to see if it improves as the patient grows. While acutely successful, eventually hemodynamics may become suboptimal if TAH is left untreated. The objective of this work aims to develop a patient-specific surgical planning framework for predicting and assessing postoperative outcomes of simple CoA repair and comprehensive repair of CoA and TAH. METHODS: The surgical planning framework consisted of virtual clamp placement, stenosis resection, and design and optimization of patient-specific aortic grafts that involved geometrical modeling of the graft and computational fluid dynamics (CFD) simulation for evaluating various surgical plans. Time-dependent CFD simulations were performed using Windkessel boundary conditions at the outlets that were obtained from patient-specific non-invasive pressure and flow data to predict hemodynamics before and after the virtual repairs. We applied the proposed framework to investigate optimal repairs for six patients (n = 6) diagnosed with both CoA and TAH. Design optimization was performed by creating a combination of a tubular graft and a waterslide patch to reconstruct the aortic arch. The surfaces of the designed graft were parameterized to optimize the shape. RESULTS: Peak systolic pressure drop (PSPD) and time-averaged wall shear stress (TAWSS) were used as performance metrics to evaluate surgical outcomes of various graft designs and implantation. The average PSPD improvements were 28% and 44% after the isolated CoA repair and comprehensive repair, respectively. Maximum values of TAWSS were decreased by 60% after CoA repair and further improved by 22% after the comprehensive repair. The oscillatory shear index was calculated and the values were confirmed to be in the normal range after the repairs. CONCLUSION: The results showed that the comprehensive repair outperforms the simple CoA repair and may be more advantageous in the long term in some patients. We demonstrated that the surgical planning and patient-specific flow simulations could potentially affect the selection and outcomes of aorta repairs.

Toward a novel soft robotic system for minimally invasive interventions.

PURPOSE: During minimally invasive surgery, surgeons maneuver tools through complex anatomies, which is difficult without the ability to control the position of the tools inside the body. A potential solution for a substantial portion of these procedures is the efficient design and control of a pneumatically actuated soft robot system. METHODS: We designed and evaluated a system to control a steerable catheter tip. A macroscale 3D printed catheter tip was designed to have two separately pressurized channels to induce bending in two directions. A motorized hand controller was developed to allow users to control the bending angle while manually inserting the steerable tip. Preliminary characterization of two catheter tip prototypes was performed and used to map desired angle inputs into pressure commands. RESULTS: The integrated robotic system allowed both a novice and a skilled surgeon to position the steerable catheter tip at the location of cylindrical targets with sub-millimeter accuracy. The novice was able to reach each target within ten seconds and the skilled surgeon within five seconds on average. CONCLUSION: This soft robotic system enables its user to simultaneously insert and bend the pneumatically actuated catheter tip with high accuracy and in a short amount of time. These results show promise concerning the development of a soft robotic system that can improve outcomes in minimally invasive interventions.

Artificial intelligence meets medical robotics.

Artificial intelligence (AI) applications in medical robots are bringing a new era to medicine. Advanced medical robots can perform diagnostic and surgical procedures, aid rehabilitation, and provide symbiotic prosthetics to replace limbs. The technology used in these devices, including computer vision, medical image analysis, haptics, navigation, precise manipulation, and machine learning (ML) , could allow autonomous robots to carry out diagnostic imaging, remote surgery, surgical subtasks, or even entire surgical procedures. Moreover, AI in rehabilitation devices and advanced prosthetics can provide individualized support, as well as improved functionality and mobility (see the figure). The combination of extraordinary advances in robotics, medicine, materials science, and computing could bring safer, more efficient, and more widely available patient care in the future. -Gemma K. Alderton.

Overcoming the Force Limitations of Magnetic Robotic Surgery: Magnetic Pulse Actuated Collisions for Tissue-Penetrating-Needle for Tetherless Interventions.

The field of magnetic robotics aims to obviate physical connections between the actuators and end-effectors. Such tetherless control may enable new ultra-minimally invasive surgical manipulations in clinical settings. While wireless actuation offers advantages in medical applications, the challenge of providing sufficient force to magnetic needles for tissue penetration remains a barrier to practical application. Applying sufficient force for tissue penetration is required for tasks such as biopsy, suturing, cutting, drug delivery, and accessing deep seated regions of complex structures in organs such as the eye. To expand the force landscape for such magnetic surgical tools, an impact-force based suture needle capable of penetrating in vitro and ex vivo samples with 3-DOF planar motion is proposed. Using custom-built 14G and 25G needles, we demonstrate generation of 410 mN penetration force, a 22.7-fold force increase with more than 20 times smaller volume compared to similar magnetically guided needles. With the MPACT-Needle, in vitro suturing of a gauze mesh onto an agar gel is demonstrated. In addition, we have reduced the tip size to 25G, which is a typical needle size for interventions in the eye, to demonstrate ex vivo penetration in a rabbit eye, mimicking procedures such as corneal injections and transscleral drug delivery.

Virtual Reality Cardiac Surgical Planning Software (CorFix) for Designing Patient-Specific Vascular Grafts: Development and Pilot Usability Study.

BACKGROUND: Patients with single ventricle heart defects receive 3 stages of operations culminating in the Fontan procedure. During the Fontan procedure, a vascular graft is sutured between the inferior vena cava and pulmonary artery to divert deoxygenated blood flow to the lungs via passive flow. Customizing the graft configuration can maximize the long-term benefits. However, planning patient-specific procedures has several challenges, including the ability for physicians to customize grafts and evaluate their hemodynamic performance. OBJECTIVE: The aim of this study was to develop a virtual reality (VR) Fontan graft modeling and evaluation software for physicians. A user study was performed to achieve 2 additional goals: (1) to evaluate the software when used by medical doctors and engineers, and (2) to explore the impact of viewing hemodynamic simulation results in numerical and graphical formats. METHODS: A total of 5 medical professionals including 4 physicians (1 fourth-year resident, 1 third-year cardiac fellow, 1 pediatric intensivist, and 1 pediatric cardiac surgeon) and 1 biomedical engineer voluntarily participated in the study. The study was pre-scripted to minimize the variability of the interactions between the experimenter and the participants. All participants were trained to use the VR gear and our software, CorFix. Each participant designed 1 bifurcated and 1 tube-shaped Fontan graft for a single patient. A hemodynamic performance evaluation was then completed, allowing the participants to further modify their tube-shaped design. The design time and hemodynamic performance for each graft design were recorded. At the end of the study, all participants were provided surveys to evaluate the usability and learnability of the software and rate the intensity of VR sickness. RESULTS: The average times for creating 1 bifurcated and 1 tube-shaped graft after a single 10-minute training session were 13.40 and 5.49 minutes, respectively, with 3 out 5 bifurcated and 1 out of 5 tube-shaped graft designs being in the benchmark range of hepatic flow distribution. Reviewing hemodynamic performance results and modifying the tube-shaped design took an average time of 2.92 minutes. Participants who modified their tube-shaped graft designs were able to improve the nonphysiologic wall shear stress (WSS) percentage by 7.02%. All tube-shaped graft designs improved the WSS percentage compared to the native surgical case of the patient. None of the designs met the benchmark indexed power loss. CONCLUSIONS: VR graft design software can quickly be taught to physicians with no engineering background or VR experience. Improving the CorFix system could improve performance of the users in customizing and optimizing grafts for patients. With graphical visualization, physicians were able to improve WSS percentage of a tube-shaped graft, lowering the chance of thrombosis. Bifurcated graft designs showed potential strength in better flow split to the lungs, reducing the risk for pulmonary arteriovenous malformations.

Deep point cloud landmark localization for fringe projection profilometry.

Point clouds have been widely used due to their information being richer than images. Fringe projection profilometry (FPP) is one of the camera-based point cloud acquisition techniques that is being developed as a vision system for robotic surgery. For semi-autonomous robotic suturing, fluorescent fiducials were previously used on a target tissue as suture landmarks. This not only increases system complexity but also imposes safety concerns. To address these problems, we propose a numerical landmark localization algorithm based on a convolutional neural network (CNN) and a conditional random field (CRF). A CNN is applied to regress landmark heatmaps from the four-channel image data generated by the FPP. A CRF leveraging both local and global shape constraints is developed to better tune the landmark coordinates, reject extra landmarks, and recover missing landmarks. The robustness of the proposed method is demonstrated through ex vivo porcine intestine landmark localization experiments.

Computational Fontan Analysis: Preserving Accuracy While Expediting Workflow.

Background: Postoperative outcomes of the Fontan operation have been linked to geometry of the cavopulmonary pathway, including graft shape after implantation. Computational fluid dynamics (CFD) simulations are used to explore different surgical options. The objective of this study is to perform a systematic in vitro validation for investigating the accuracy and efficiency of CFD simulation to predict Fontan hemodynamics. Methods: CFD simulations were performed to measure indexed power loss (iPL) and hepatic flow distribution (HFD) in 10 patient-specific Fontan models, with varying mesh and numerical solvers. The results were compared with a novel in vitro flow loop setup with 3D printed Fontan models. A high-resolution differential pressure sensor was used to measure the pressure drop for validating iPL predictions. Microparticles with particle filtering system were used to measure HFD. The computational time was measured for a representative Fontan model with different mesh sizes and numerical solvers. Results: When compared to in vitro setup, variations in CFD mesh sizes had significant effect on HFD (P = .0002) but no significant impact on iPL (P = .069). Numerical solvers had no significant impact in both iPL (P = .50) and HFD (P = .55). A transient solver with 0.5 mm mesh size requires computational time 100 times more than a steady solver with 2.5 mm mesh size to generate similar results. Conclusions: The predictive value of CFD for Fontan planning can be validated against an in vitro flow loop. The prediction accuracy can be affected by the mesh size, model shape complexity, and flow competition.

Semi-Automatic Planning and Three-Dimensional Electrospinning of Patient-Specific Grafts for Fontan Surgery.

This paper proposes a semi-automatic Fontan surgery planning method for designing and manufacturing hemodynamically optimized patient-specific grafts. Fontan surgery is a palliative procedure for patients with a single ventricle heart defect by creating a new path using a vascular graft for the deoxygenated blood to be directed to the lungs, bypassing the heart. However, designing patient-specific grafts with optimized hemodynamic performance is a complex task due to the variety of patient-specific anatomies, confined surgical planning space, and the requirement of simultaneously considering multiple design criteria for vascular graft optimization. To address these challenges, we used parameterized Fontan pathways to explore patient-specific vascular graft design spaces and search for optimal solutions by formulating a nonlinear constrained optimization problem, which minimizes indexed power loss (iPL) of the Fontan model by constraining hepatic flow distribution (HFD), percentage of abnormal wall shear stress (%WSS) and geometric interference between Fontan pathways and the heart models (InDep) within clinically acceptable thresholds. Gaussian process regression was employed to build surrogate models of the hemodynamic parameters as well as InDep and [Formula: see text] (conduit model smoothness indicator) for optimization by pattern search. We tested the proposed method on two patient-specific models (n=2). The results showed the automatically optimized (AutoOpt) Fontan models hemodynamically outperformed or at least are comparable to manually optimized Fontan models with significantly reduced surgical planning time (15 hours versus over 2 weeks). We also demonstrated feasibility of manufacturing the AutoOpt Fontan conduits by using electrospun nanofibers.

Location matters: Offset in tissue-engineered vascular graft implantation location affects wall shear stress in porcine models.

Objective: Although surgical simulation using computational fluid dynamics has advanced, little is known about the accuracy of cardiac surgical procedures after patient-specific design. We evaluated the effects of discrepancies in location for patient-specific simulation and actual implantation on hemodynamic performance of patient-specific tissue-engineered vascular grafts (TEVGs) in porcine models. Methods: Magnetic resonance angiography and 4-dimensional (4D) flow data were acquired in porcine models (n = 11) to create individualized TEVGs. Graft shapes were optimized and manufactured by electrospinning bioresorbable material onto a metal mandrel. TEVGs were implanted 1 or 3 months postimaging, and postoperative magnetic resonance angiography and 4D flow data were obtained and segmented. Displacement between intended and observed TEVG position was determined through center of mass analysis. Hemodynamic data were obtained from 4D flow analysis. Displacement and hemodynamic data were compared using linear regression. Results: Patient-specific TEVGs were displaced between 1 and 8 mm during implantation compared with their surgically simulated, intended locations. Greater offset between intended and observed position correlated with greater wall shear stress (WSS) in postoperative vasculature (P < .01). Grafts that were implanted closer to their intended locations showed decreased WSS. Conclusions: Patient-specific TEVGs are designed for precise locations to help optimize hemodynamic performance. However, if TEVGs were implanted far from their intended location, worse WSS was observed. This underscores the importance of not only patient-specific design but also precision-guided implantation to optimize hemodynamics in cardiac surgery and increase reproducibility of surgical simulation.

A Confidence-Based Supervised-Autonomous Control Strategy for Robotic Vaginal Cuff Closure.

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.

Application of noise-reduction techniques to machine learning algorithms for breast cancer tumor identification.

The application of machine learning (ML) techniques to digitized images of biopsied cells for breast cancer diagnosis is an active area of research. We hypothesized that reducing noise in the data would lead to an increase in classification accuracies. To test this hypothesis, we first compared several classification techniques in their ability to discriminate between malignant and benign breast cancer tumors using the Wisconsin Breast Cancer Data Set and subsequently evaluated the effect of noise reduction techniques on model accuracies. We applied two noise-reduction techniques based on Principal Component Analysis - dimensionality reduction and outlier removal - to a comprehensive list of ML algorithms with different learning paradigms including Decision Trees (fine, medium, coarse), dimensionality reduction techniques (Linear Discriminant Analysis, Quadratic Discriminant Analysis, Partial Least Squares-Discriminant Analysis), logistic Regression, Bayesian techniques (Gaussian Naive, Kernel Naive), Support Vector Machines (Linear, Quadratic, Cubic, Gaussian), instance-based techniques (fine, medium, coarse, cosine, cubic, and weighted K-Nearest Neighbors), and Artificial Neural Networks. Results showed that noise removal through dimensionality reduction is most effective when using a cross-validated number of principal components, and accuracies surpassing 99% across all ML models are obtained when both noise-reduction techniques are applied sequentially. Even though such a high accuracy has been demonstrated in few instances for specific algorithms, the methodology proposed herein is the first published report demonstrating the applicability of a technique to a wide range of ML models to achieve high accuracies. We show that dimensionality reduction and outlier analysis can be used as effective approaches to improve discrimination accuracies. Also, dimensionality reduction through a cross-validated number of principal components can provide an effective framework for reducing noise in the data prior to applying a ML algorithm.

Responding to a Pandemic: COVID-19 Projects in the Malone Center.

As the scope and scale of the COVID-19 pandemic became clear in early March of 2020, the faculty of the Malone Center engaged in several projects aimed at addressing both immediate and long-term implications of COVID-19. In this article, we briefly outline the processes that we engaged in to identify areas of need, the projects that emerged, and the results of those projects. As we write, some of these projects have reached a natural termination point, whereas others continue. We identify some of the factors that led to projects that moved to implementation, as well as factors that led projects to fail to progress or to be abandoned.

The Use of Three-dimensional Visualization Techniques for Prostate Procedures: A Systematic Review.

CONTEXT: As an emerging technique, three-dimensional (3D) visualization has become more popular and can facilitate education, training, surgical planning, and intraoperative guidance for prostate cancer surgery. OBJECTIVE: In this review, we aim to present the impact of 3D printing, virtual reality (VR), and augmented reality (AR) techniques for prostate cancer procedures, specifically prostate biopsy and radical prostatectomy (RP). EVIDENCE ACQUISITION: A systematic review was performed by two investigators according to the Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) criteria. EVIDENCE SYNTHESIS: A total of 541 papers were identified in PubMed, Scopus, and Embase. Of these, 53 studies were identified for detailed review and 25 were qualified. Two more studies were identified from the references; thus, 27 studies were finally included in this systematic review. Nine papers reported on the use of 3D reconstructed models, mainly in education/training and intraoperative guidance; nine reported on VR, focusing on simulation training model and intraoperative guidance; and nine reported on AR technique with its best indication for surgical guidance in robotic RP. CONCLUSIONS: Three-dimensional visualization techniques have gradually been introduced and developed in prostate procedures, and demonstrate potential utility not only for education/training, but also for surgical planning and intraoperative guidance. Prospective studies are needed to demonstrate clinical utility and validation of these technologies. PATIENT SUMMARY: Despite low-quality evidence, promising signals were identified to demonstrate that three-dimensional visualization could help facilitate prostate procedures, in terms of education/training, surgical planning, and intraoperative guidance. It is still in a very early stage, and more studies need to be conducted to justify its widespread use.

Vaginal Cuff Closure with Dual-Arm Robot and Near-Infrared Fluorescent Sutures.

This paper presents a dual-arm suturing robot. We extend the Smart Tissue Autonomous Robot (STAR) with a second robot manipulator, whose purpose is to manage loose suture thread, a task that was previously executed by a human assistant. We also introduce novel near-infrared fluorescent (NIRF) sutures that are automatically segmented and delimit the boundaries of the suturing task. During ex-vivo experiments of porcine models, our results demonstrate that this new system is capable of outperforming human surgeons in all but one metric for the task of vaginal cuff closure (porcine model) and is more consistent in every aspect of the task. We also present results to demonstrate that the system can perform a vaginal cuff closure during an in-vivo experiment (porcine model).

Aorta size mismatch predicts decreased exercise capacity in patients with successfully repaired coarctation of the aorta.

OBJECTIVE: Coarctation of the aorta (CoA) is associated with decreased exercise capacity despite successful repair with no residual stenosis; however, the hemodynamic mechanism remains unknown. This study aims to correlate aortic arch geometry with exercise capacity in patients with successfully repaired CoA and explain hemodynamic changes using 3-dimensional-printed aorta models in a mock circulatory flow loop. METHODS: A retrospective chart review identified patients with CoA repair who had cardiac magnetic resonance imaging and an exercise stress test. Measurements included aorta diameters, arch height to diameter ratio, left ventricular function, and percent descending aorta (%DAo) flow. Each aorta was printed 3-dimensionally for the flow loop. Flow and pressure were measured at the ascending aorta (AAo) and DAo during simulated rest and exercise. Measurements were correlated with percent predicted peak oxygen consumption (VO2 max). RESULTS: Fifteen patients (mean age 26.8 ± 8.6 years) had a VO2 max between 47% and 126% predicted (mean 92 ± 20%) with normal left ventricular function. DAo diameter and %DAo flow positively correlated with VO2 (P = .007 and P = .04, respectively). AAo to DAo diameter ratio (DAAo/DDAo) negatively correlated with VO2 (P < .001). From flow loop simulations, the ratio of %DAo flow in exercise to rest negatively correlated with VO2 (P = .02) and positively correlated with DAAo/DDAo (P < .01). CONCLUSIONS: This study suggests aorta size mismatch (DAAo/DDAo) is a novel, clinically important measurement predicting exercise capacity in patients with successful CoA repair, likely due to increased resistance and altered flow distribution. Aorta size mismatch and %DAo flow are targets for further clinical evaluation in repaired CoA.

Supervised Autonomous Electrosurgery for Soft Tissue Resection.

Surgical resection is the current clinical standard of care for treating squamous cell carcinoma. Maintaining an adequate tumor resection margin is the key to a good surgical outcome, but tumor edge delineation errors are inevitable with manual surgery due to difficulty in visualization and hand-eye coordination. Surgical automation is a growing field of robotics to relieve surgeon burdens and to achieve a consistent and potentially better surgical outcome. This paper reports a novel robotic supervised autonomous electrosurgery technique for soft tissue resection achieving millimeter accuracy. The tumor resection procedure is decomposed to the subtask level for a more direct understanding and automation. A 4-DOF suction system is developed, and integrated with a 6-DOF electrocautery robot to perform resection experiments. A novel near-infrared fluorescent marker is manually dispensed on cadaver samples to define a pseudotumor, and intraoperatively tracked using a dual-camera system. The autonomous dual-robot resection cooperation workflow is proposed and evaluated in this study. The integrated system achieves autonomous localization of the pseudotumor by tracking the near-infrared marker, and performs supervised autonomous resection in cadaver porcine tongues (N=3). The three pseudotumors were successfully removed from porcine samples. The evaluated average surface and depth resection errors are 1.19 and 1.83mm, respectively. This work is an essential step towards autonomous tumor resections.