In percutaneous intervention for treatment of coronary plaques, guidewire navigation is a primary procedure for stent delivery. Steering a flexible guidewire within coronary arteries requires considerable training, and the non-linearity between the control operation and the movement of the guidewire makes precise manipulation difficult. Here, we introduce a deep reinforcement learning (RL) framework for autonomous guidewire navigation in a robot-assisted coronary intervention. Using Rainbow, a segment-wise learning approach is applied to determine how best to accelerate training using human demonstrations, transfer learning, and weight initialization. ‘State’ for RL is customized as a focus window near the guidewire tip, and subgoals are placed to mitigate a sparse reward problem. The RL agent improves performance, eventually enabling the guidewire to reach all valid targets in ‘stable’ phase. For the last 300 out of 1000 episodes, the success rates of the guidewire navigation to the distal-main and side targets were 98% and 99% in 2D and 3D phantoms, respectively. Our framework opens a new direction in the automation of robot-assisted intervention, providing guidance on RL in physical spaces involving mechanical fatigue.

Conventional vascular intervention procedures present issues including X-ray exposure during operation, and an experience-dependent success rate and clinical outcome. This paper presents a novel robotic system using modularized bi-motional roller cartridge assemblies for robotic vascular interventions, specifically percutaneous coronary interventions (PCIs). The patient-side robot manipulates instruments such as the guiding catheter, guidewire, balloon/stent catheter, and diagnostic sensor catheter via commands from the user interface device, which is controlled by the physician. The proposed roller cartridge assembly can accommodate instruments of various sizes with an active clamping mechanism, and implements simultaneous translation and rotation motions. It also implements force feedback in the physician-side system, to effectively monitor the patient-side system’s status. The positioning accuracy and precision in using the robotic system showed satisfactory performance in a phantom-based test. It was also confirmed, through animal experiments and a pilot clinical trial, that the system demonstrates feasibility for clinical use.

Nanorods based on ZnO for surface enhanced Raman spectroscopy are promising for the non-invasive and rapid detection of biomarkers and diagnosis of disease. However, optimization of nanorod and coating parameters is essential to their practical application. With the goal of establishing a baseline for early detection in biological applications, gold-coated ZnO nanorods were grown and coated to form porous structures. Prior to gold deposition, the grown nanorods were 30–50 nm in diameter and 500–600 nm in length. Gold coatings were grown on the nanorod structure to a series of thicknesses between 100 and 300 nm. A gold coating of 200 nm was found to optimize the Rhodamine B model analyte signal, while performance for rat urine depended on the biomarkers to be detected. These results establish design guidelines for future use of Au-ZnO nanorods in the study and early diagnosis of inflammatory diseases.

Background: Endoscopes are widely used for visualizing the respiratory tract, urinary tract, uterus, and gastrointestinal tracts. Despite high demand, people in underdeveloped and developing countries cannot obtain proper access to endoscopy. Moreover, commercially available endoscopes are mostly nonarticulable as well as not actively controlled, limiting their use. Articulating endoscopes are required for some diagnosis procedures, due to their ability to image wide areas of internal organs. Furthermore, actively controlled articulating endoscopes are less likely to harm the lumen than rigid endoscopes because they can avoid contact with endothelial tissues. Objective: The study aimed to demonstrate the feasibility and acceptability of smartphone-based wide-field articulable endoscope system for minimally invasive clinical applications in developing and less developed countries. Methods: A thin articulable endoscope system that can be attached to and actively controlled by a smartphone was designed and constructed. The system consists of a flexible endoscopic probe with a continuum mechanism, 4 motor modules for articulation, a microprocessor for controlling the motor with a smartphone, and a homebuilt app for streaming, capturing, adjusting images and video, and controlling the motor module with a joystick-like user interface. The smartphone and motor module are connected via an integrated C-type On-The-Go (OTG) USB hub. Results: We tested the device in several human-organ phantoms to evaluate the usability and utility of the smartphone-based articulating endoscope system. The resolution (960 × 720 pixels) of the device was found to be acceptable for medical diagnosis. The maximum bending angle of 110° was designed. The distance from the base of the articulating module to the tip of the endoscope was 45 mm. The angle of the virtual arc was 40.0°, for a curvature of 0.013. The finest articulation resolution was 8.9°. The articulating module succeeded in imaging all 8 octants of a spherical target, as well as all 4 quadrants of the indices marked in human phantoms. Conclusions: The portable wide-field endoscope was successfully controlled using a smartphone, yielding clear images with a resolution of 960 × 720 pixels at realistic focal distances. Actively and precisely controlled articulating movements have resulted in minimally invasive monitoring in the narrow space of internal organs providing a wide-area view. We found our smartphone-based active articulated endoscope to be suitable for point-of-care applications in developing and less developed countries.

Although the use of the surgical robot is rapidly expanding for various medical treatments, there still exist safety issues and concerns about robot-assisted surgeries due to limited vision through a laparoscope, which may cause compromised situation awareness and surgical errors requiring rapid emergency conversion to open surgery. To assist surgeon’s situation awareness and preventive emergency response, this study proposes situation information guidance through a vision-based common algorithm architecture for automatic detection and tracking of intraoperative hemorrhage and surgical instruments. The proposed common architecture comprises the location of the object of interest using feature texture, morphological information, and the tracking of the object based on Kalman filter for robustness with reduced error. The average recall and precision of the instrument detection in four prostate surgery videos were 96% and 86%, and the accuracy of the hemorrhage detection in two prostate surgery videos was 98%. Results demonstrate the robustness of the automatic intraoperative object detection and tracking which can be used to enhance the surgeon’s preventive state recognition during robot-assisted surgery.