Percutaneous coronary intervention (PCI) involves the insertion of a catheter or guidewire into a blood vessel of a patient, which poses a problem as a doctor is exposed to radiation during the procedure. The use of assistive robots has been proposed to address this issue. Furthermore, recent research is progressing toward complete autonomous navigation using deep reinforcement learning (DRL). Nevertheless, existing algorithms face limitations when operating in numerous unseen environments close to real PCI. This study proposes a robust DRL framework for image-based guidewire navigation to overcome the limitation. We introduce a subtasks strategy and domain randomization to improve robustness in various environments. The subtasks strategy consistently addresses complex global tasks by breaking them into subtasks designed using local maps, allowing them to be robustly solved by a single agent. Domain randomization is applied to handle real PCI issues, including variations in vessel geometry, guidewire deformation, and camera settings. By integrating the two novel methods, our DRL algorithm demonstrates superior performance compared to existing methods across various challenging simulation and phantom environments, validating its effectiveness in real-world scenarios. A video of our experiment is available at https://youtu.be/93Q88gESzOY.

Percutaneous coronary intervention (PCI) is a frequently used surgical treatment for cardiovascular disease, one of the leading cause of death in the world. In traditional PCI, a doctor navigates a thin guidewire in a patient's vessel toward a target location by looking into live X-ray angiogram images of the patient. Recently, researchers are using reinforcement learning to automate this guidewire navigation process without attaching any sensor to the guidewire tip. These researchers use a real vessel phantom to train their behavior policy using reinforcement learning. Training a reinforcement learning algorithm on a real setup can give a good guidewire control on that setup, but it is under question whether the trained algorithm can be applied to other vessel structures. We can make various vessel phantoms and train the algorithm on the setups, but it can be really time and money consuming. In this paper, we devise a method for sim-to-real transfer of a guidewire navigation trained by reinforcement learning using only images. We pretrain our behavior policy using data collected by running an expert algorithm in the virtual environment. Then, we train the behavior policy by deep deterministic policy gradient (DDPG) in a virtual environment. With behavior cloning, our method learns to successfully navigate a guidewire in much shorter time than training DDPG from scratch without behavior cloning. After done with the training, we transfer the behavior policy trained in the virtual environment to the guidewire navigation in a real vessel phantom. Our trained behavior policy navigates the guidewire to destinations successfully in all test episodes and navigates faster than the expert algorithm. Experiment video is available at: https: //youtu.be/HCEbIhZsXqw

A web-based database system for intelligent remote monitoring of an artificial heart has been developed. It is important for patients with an artificial heart implant to be discharged from the hospital after an appropriate stabilization period for better recovery and quality of life. Reliable continuous remote monitoring systems for these patients with life support devices are gaining practical meaning. The authors have developed a remote monitoring system for this purpose that consists of a portable/desktop monitoring terminal, a database for continuous recording of patient and device status, a web-based data access system with which clinicians can access real-time patient and device status data and past history data, and an intelligent diagnosis algorithm module that noninvasively estimates blood pump output and makes automatic classification of the device status. The system has been tested with data generation emulators installed on remote sites for simulation study, and in two cases of animal experiments conducted at remote facilities. The system showed acceptable functionality and reliability. The intelligence algorithm also showed acceptable practicality in an application to animal experiment data.