Automatic tool segmentation and tracking during robotic intravascular catheterization for cardiac interventions

Endovascular Tool Segmentation with Multi-lateral Branched Network during Robot-assisted Catheterization

Robot-assisted catheterization is routinely carried out for intervention of cardiovascular diseases. Meanwhile, the success of endovascular tool navigation depends on visualization and tracking cues available in the robotic platform. Currently, real-time motion analytics are lacking, while poor illumination during fluoroscopy affects existing physics- and learning-based methods used for tool segmentation. A multi-lateral branched network (MLB-Net) is herein proposed for tool segmentation in cardiovascular angiograms. The model has an encoder with multi-lateral separable convolutions and a pyramid decoder. Model training and validation are done on 1320 angiograms obtained during robot-assisted catheterization in rabbit. Model performance, explained with F1-score of 89.01% and mean intersection-over-union of 90.05% on 330 frames, indicates the model's robustness for guidewire segmentation in angiograms. The MLB-Net offers better performance than the state-of-the-art segmentation models such as U-Net, U-Net++ and DeepLabV3. Thus, it could provide basis for endovascular tool tracking and surgical scene analytics during cardiovascular interventions.

Guidewire Endpoint Detection Based on Pixel Adjacent Relation in Robot-assisted Cardiovascular Interventions

Visualization of endovascular tools like guidewire and catheter is essential for procedural success of endovascular interventions. This requires tracking the tool pixels and motion during catheterization; however, detecting the endpoints of the endovascular tools is challenging due to their small size, thin appearance, and flexibility. As this still limit the performances of existing methods used for endovascular tool segmentation, predicting correct object location could provide ways forward. In this paper, we proposed a neighborhood-based method for detecting guidewire endpoints in X-ray angiograms. Typically, it consists of pixel-level segmentation and a post-segmentation step that is based on adjacency relationships of pixels in a given neighborhood. The latter includes skeletonization to predict endpoint pixels of guidewire. The method is evaluated with proprietary guidewire dataset obtained during in-vivo study in six rabbits, and it shows a high segmentation performance characterized with precision of 87.87% and recall of 90.53%, and low detection error with a mean pixel error of 2.26±0.14 pixels. We compared our method with four state-of-the-art detection methods and found it to exhibit the best detection performance. This neighborhood-based detection method can be generalized for other surgical tool detection and in related computer vision tasks.Clinical Relevance- The proposed method can be provided with better tool tracking and visualization systems during robot-assisted intravascular interventional surgery.

Technical and Clinical Progress on Robot-Assisted Endovascular Interventions: A Review

Prior methods of patient care have changed in recent years due to the availability of minimally invasive surgical platforms for endovascular interventions. These platforms have demonstrated the ability to improve patients' vascular intervention outcomes, and global morbidities and mortalities from vascular disease are decreasing. Nonetheless, there are still concerns about the long-term effects of exposing interventionalists and patients to the operational hazards in the cath lab, and the perioperative risks that patients undergo. For these reasons, robot-assisted vascular interventions were developed to provide interventionalists with the ability to perform minimally invasive procedures with improved surgical workflow. We conducted a thorough literature search and presented a review of 130 studies published within the last 20 years that focused on robot-assisted endovascular interventions and are closely related to the current gains and obstacles of vascular interventional robots published up to 2022. We assessed both the research-based prototypes and commercial products, with an emphasis on their technical characteristics and application domains. Furthermore, we outlined how the robotic platforms enhanced both surgeons' and patients' perioperative experiences of robot-assisted vascular interventions. Finally, we summarized our findings and proposed three key milestones that could improve the development of the next-generation vascular interventional robots.

Advances in the application of robotic surgical systems in gastric cancer: A narrative review

Gastric cancer is one of the common malignant tumors in the gastrointestinal tract, and surgery is currently an important treatment for progressive gastric cancer. With the development of technology, the simultaneous maturation of artificial intelligence (AI), fifth-generation (5G) telecommunication networks and the internet of things (IOT) has brought significant efficacy and new opportunities for the surgical treatment of gastric malignancies. The combination of 5G network and remote surgical robotic system is the future trend of radical gastric cancer surgery, and the "unmanned" treatment mode of fully automated robotic gastric cancer radical surgery will be realized soon.

A Hybrid Microstructure Piezoresistive Sensor with Machine Learning Approach for Gesture Recognition

Developments in flexible electronics have adopted various approaches which have enhanced the applicability of human–machine interface fields. Recently, microstructural integration and hybrid functional materials were designed for realizing human somatosensory. Nonetheless, designing tactile sensors with smart structures using facile and low-cost fabrication processes remains challenging. Furthermore, using the sensors for recognizing stimuli and feedback applications remains poorly validated. In this study, a highly flexible piezoresistive tactile sensor was developed by homogeneously dispersing carbon black (CB) in a microstructure porous sugar/PDMS-based sponge. Owning to its high flexibility and softness, the sensor can be mounted on human or robotic systems for different clinical applications. We validated the applicability of the proposed sensor by applying it to recognizing grasp and release forces in an open setting and to classifying hand motions that surgeons apply on the master interface of a robotic system during intravascular catheterization. For this purpose, we implemented the long short-term memory (LSTM)-dense classification model and five traditional machine learning methods, namely, support vector machine, multilayer perceptron, decision tree, and k-nearest neighbor. The models were used to classify the different hand gestures obtained in an open-setting experiment. Amongst all, the LSTM-dense method yielded the highest overall recognition accuracy (87.38%). Nevertheless, the performance of the other models was in a similar range, showing that our sensor structure can be applied in intelligence sensing or tactile feedback systems. Secondly, the sensor prototype was applied to analyze the motions made while manipulating an interventional robot. We analyzed the displacement and velocity of the master interface during typical axial (push/pull) and radial operations with the robot. The results obtained show that the sensor is capable of recording unique patterns during different operations. Thus, a combination of the flexible wearable sensors and machine learning could yield a future generation of flexible materials and artificial intelligence of things (AIoT) devices.