Reinforcement of Dextran Methacrylate-Based Hydrogel, Semi-IPN, and IPN with Multivalent Crosslinkers.

The need for new biomaterials to meet the needs of advanced healthcare therapies is constantly increasing. Polysaccharide-based matrices are considered extremely promising because of their biocompatibility and soft structure; however, their use is limited by their poor mechanical properties. In this light, a strategy for the reinforcement of dextran-based hydrogels and interpenetrated polymer networks (semi-IPNs and IPNs) is proposed, which will introduce multifunctional crosslinkers that can modify the network crosslinking density.

Towards safer robot-assisted surgery: A markerless augmented reality framework.

Robot-assisted surgery is rapidly developing in the medical field, and the integration of augmented reality shows the potential to improve the operation performance of surgeons by providing more visual information. In this paper, we proposed a markerless augmented reality framework to enhance safety by avoiding intra-operative bleeding, which is a high risk caused by collision between surgical instruments and delicate blood vessels (arteries or veins). Advanced stereo reconstruction and segmentation networks are compared to find the best combination to reconstruct the intra-operative blood vessel in 3D space for registration with the pre-operative model, and the minimum distance detection between the instruments and the blood vessel is implemented.

Spatio-temporal layers based intra-operative stereo depth estimation network via hierarchical prediction and progressive training.

Background And Objective: Safety of robotic surgery can be enhanced through augmented vision or artificial constraints to the robotl motion, and intra-operative depth estimation is the cornerstone of these applications because it provides precise position information of surgical scenes in 3D space. High-quality depth estimation of endoscopic scenes has been a valuable issue, and the development of deep learning provides more possibility and potential to address this issue.

Methods: In this paper, a deep learning-based approach is proposed to recover 3D information of intra-operative scenes.

IMU-based classification of resistive exercises for real-time training monitoring on board the international space station with potential telemedicine spin-off.

The microgravity exposure that astronauts undergo during space missions lasting up to 6 months induces biochemical and physiological changes potentially impacting on their health. As a countermeasure, astronauts perform an in-flight training program consisting in different resistive exercises. To train optimally and safely, astronauts need guidance by on-ground specialists via a real-time audio/video system that, however, is subject to a communication delay that increases in proportion to the distance between sender and receiver.

FRSR: Framework for real-time scene reconstruction in robot-assisted minimally invasive surgery.

3D reconstruction of the intra-operative scenes provides precise position information which is the foundation of various safety related applications in robot-assisted surgery, such as augmented reality. Herein, a framework integrated into a known surgical system is proposed to enhance the safety of robotic surgery. In this paper, we present a scene reconstruction framework to restore the 3D information of the surgical site in real time.