Robot-assisted catheterization has garnered a good attention for its potentials in treating cardiovascular diseases. However, advancing surgeon-robot collaboration still requires further research, particularly on task-specific automation. For instance, automated tool segmentation can assist surgeons in visualizing and tracking endovascular tools during procedures. While learning-based models have demonstrated state-of-the-art segmentation performances, generating ground-truth labels for fully-supervised methods is laborintensive, time consuming, and costly. In this study, we developed a weakly-supervised learning method that is based on multi-lateral pseudo labeling for tool segmentation in cardiovascular angiogram datasets. The method utilizes a modified U-Net architecture featuring one encoder and multiple laterally branched decoders. The decoders generate diverse pseudo labels under different perturbations to augment the available partial annotation for model training. A mixed loss function with shared consistency was adapted for this purpose. The weakly-supervised model was trained end-to-end and validated using partially annotated angiogram data from three cardiovascular catheterization procedures. Validation results show that the weakly-supervised model could perform closer to fully-supervised models. Furthermore, the proposed multi-lateral approach outperforms three well known weakly-supervised learning methods, offering the highest segmentation performance across the three angiogram datasets. Numerous ablation studies confirmed the model's consistent performance under different settings. Finally, the model was applied for tool segmentation in a robot-assisted catheterization experiments. The model enhanced visualization with high connectivity indices for guidewire and catheter, and a mean segmentation time of 35.26±11.29 ms per frame. This study provides a fast, stable, and less expensive method for segmentation and visualization of endovascular tools in robot-assisted cardiac catheterization.

Robot-assisted catheterization offers a promising technique for cardiovascular interventions, addressing the limitations of manual interventional surgery, where precise tool manipulation is critical. In remote-control robotic systems, the lack of force feedback and imprecise navigation challenge cooperation between the surgeon and robot. This study proposes a manipulation-based evaluation framework to assess the cooperative performance between different operators and robot using kinesthetic, kinematic, and haptic data from multi-sensor technologies. The proposed evaluation framework achieves a recognition accuracy of 99.99% in assessing the cooperation between operator and robot. Additionally, the study investigates the impact of delay factors, considering no delay, constant delay, and variable delay, on cooperation characteristics. The findings suggest that variable delay contributes to improved cooperation performance between operator and robot in a primary-secondary isomorphic robotic system, compared to a constant delay factor. Furthermore, operators with experience in manual percutaneous coronary interventions exhibit significantly better cooperative manipulate on with the robot system than those without such experience, with respective synergy ratios of 89.66%, 90.28%, and 91.12% based on the three aspects of delay consideration. Moreover, the study explores interaction information, including distal force of tools-tissue and contact force of hand-control-ring, to understand how operators with different technical skills adjust their control strategy to prevent damage to the vascular vessel caused by excessive force while ensuring enough tension to navigate complex paths. The findings highlight the potential of variable delay to enhance cooperative control strategies in robotic catheterization systems, providing a basis for optimizing surgeon-robot collaboration in cardiovascular interventions.

change of blood glucose (BG) level stimulates the autonomic nervous system leading to variation in both human's electrocardiogram (ECG) and photoplethysmogram (PPG). In this article, we aimed to construct a novel multimodal framework based on ECG and PPG signal fusion to establish a universal BG monitoring model. This is proposed as a spatiotemporal decision fusion strategy that uses weight-based Choquet integral for BG monitoring. Specifically, the multimodal framework performs three-level fusion. First, ECG and PPG signals are collected and coupled into different pools. Second, the temporal statistical features and spatial morphological features in the ECG and PPG signals are extracted through numerical analysis and residual networks, respectively. Furthermore, the suitable temporal statistical features are determined with three feature selection techniques, and the spatial morphological features are compressed by deep neural networks (DNNs). Lastly, weight-based Choquet integral multimodel fusion is integrated for coupling different BG monitoring algorithms based on the temporal statistical features and spatial morphological features. To verify the feasibility of the model, a total of 103 days of ECG and PPG signals encompassing 21 participants were collected in this article. The BG levels of participants ranged between 2.2 and 21.8 mmol/L. The results obtained show that the proposed model has excellent BG monitoring performance with a root-mean-square error (RMSE) of 1.49 mmol/L, mean absolute relative difference (MARD) of 13.42%, and Zone A + B of 99.49% in tenfold cross-validation. Therefore, we conclude that the proposed fusion approach for BG monitoring has potentials in practical applications of diabetes management.

Robot-assisted intravascular interventions have evolved as unique treatments approach for cardiovascular diseases. However, the technology currently has low potentials for catheterization skill evaluation, slow learning curve, and inability to transfer experience gained from manual interventions. This study proposes a new weighting-based deep ensemble model for recognizing interventionalists' hand motions in manual and robotic intravascular catheterization. The model has a module of neural layers for extracting features in electromyography data, and an ensemble of machine learning methods for classifying interventionalists' hand gestures as one of the six hand motions used during catheterization. A soft-weighting technique is applied to guide the contributions of each base learners. The model is validated with electromyography data recorded duringin-vitroandin-vivotrials and labeled asmany-to-onesequences. Results obtained show the proposed model could achieve 97.52% and 47.80% recognition performances on test samples in thein-vitroandin-vivodata, respectively. For the latter, transfer learning was applied to update weights from thein-vitrodata, and the retrained model was used for recognizing the hand motions in thein-vivodata. The weighting-based ensemble was evaluated against the base learners and the results obtained shows it has a more stable performance across the six hand motion classes. Also, the proposed model was compared with four existing methods used for hand motion recognition in intravascular catheterization. The results obtained show our model has the best recognition performances for both thein-vitroandin-vivocatheterization datasets. This study is developed toward increasing interventionalists' skills in robot-assisted catheterization.

Recently, flexible robotic systems are developed to enhance minimally invasive interventions on internal organs located in confined areas of human body. These surgical devices are designed to navigate anatomical pathways via single-port access, such as natural orifices or minimal incisions and intraluminal interventions. With improved precision, spatial flexibility and dexterity, the robotic technology can enhance surgery such that minimally invasive flexible access would become a faster, safer, and more convenient method for intra-body interventions without multiple or wide incisions. However, a lot of works are still required for global acceptance of existing flexible robotic surgical platforms. This review provides extended insights on the design details of two types of flexible robotic systems used for endoscopic and endovascular procedures. As of today, several prototypes of both platforms have been proposed; however, their global acceptability and applicability remains very low. To address these, we present an extensive review on design constraints and control methods which are vital for safer, faster, and better operation of the flexible robotic systems in minimally invasive surgery (MIS). Finally, research trends of flexible robotic systems and their clinical application status in MIS are discussed along with some of the technical and technological challenges hindering their prominence.

Application of intravascular catheterization, a vital task used in minimally invasive vascular surgery, has been hindered by lack of distal force sensor with miniaturized size and millinewton sensing capability. Thus, development of distal sensor for evaluating tool-vessel force interactions during robot-assisted intravascular interventions remains a research area in minimally invasive surgery. In this study, a millinewton force sensor is developed by integrating optical fibers with bragg grating in an isotonic 3D-printed flexure. The miniaturized sensor is calibrated in an experiment for 1D distal force sensing application in PCI procedures, and performance of the sensor is evaluated against that of direct FBG-pasting method. Results from the study shows that the designed sensor shows a higher repeatability and stability with a millinewton resolution in the flexure compartment. Thus, it can be suitably used for distal catheter-tip force sensing during intravascular catheterization.

Recently, applications of robotic device is showing greater advances in surgery. While robotic catheterization has been embraced to reduce the operational challenges (radiation and orthopedic hazards) inherent with percutaneous coronary interventions (PCIs), robot-based cardiac interventions are still limited to very few clinical centers in the world. In this paper, the development and application of a robotic PCI system for intravascular catheterization is presented. The robotic system is setup with underactuated master-and-slave devices and a direct control model designed based on mapping unit scales of the master displacement to trigger the slave robot for intravascular catheterization. To validate the robotic system, in-vitro trials are observed in a human-like silicone-based vascular pathway with aortic stenosis. The master-slave robotic system was successfully used to cannulate the stenotic vascular pathway with guidewire and catheter. Thus, it can be suitably adapted for intravascular catheterization during PCI and related cardiac interventions.

Despite the important role played by muscle co-contraction in stabilizing and stiffening the spine during daily activities, the effects of multi-movement models on the lumbar co-contraction are yet to be explored. This study explores the co-contraction features of lumbar muscle in subjects with and without low back pain while they perform four different movements namely forward, backward, left flexion and right flexion lumbar actions. Surface electromyography (EMG) signals of three paired lumbar muscles were measured from a total number of 60 subjects while they performed specified movement models. Co-contraction ratio (CCR), defined as ratio of normalized integration of antagonist EMG activities to the total muscle activities, were accessed, and questionnaires about pain intensity were collected with visual analogue scale (VAS). The results showed that the CCR of LBP at forward (p = 0.007) and right flexion (p = 0.011) models was significantly greater than that of healthy controls, respectively. Also, CCR was significantly different among forward, backward, left flexion and right flexion models (p<;0.05). Finally, co-contraction patterns from LBP subjects reveal disordered neuromuscular control in regulating the stiffness of lumbar spine.