Autonomous guidewire navigation in a two dimensional vascular phantom

The treatment of cerebro- and cardiovascular diseases requires complex and challenging navigation of a catheter. Previous attempts to automate catheter navigation lack the ability to be generalizable. Methods of Deep Reinforcement Learning show promising results and may be the key to automate catheter navigation through the tortuous vascular tree. This work investigates Deep Reinforcement Learning for guidewire manipulation in a complex and rigid vascular model in 2D. The neural network trained by Deep Deterministic Policy Gradients with Hindsight Experience Replay performs well on the low-level control task, however the high-level control of the path planning must be improved further

Hydraulically-actuated compliant revolute joint for medical robotic systems based on multimaterial additive manufacturing

IEEE International Conference on Robotics and Automation (ICRA), Montréal, Canada, janvier 2019 Research team : AV

Hydraulically-actuated compliant revolute joint for medical robotic systems based on multimaterial additive manufacturing

IEEE International Conference on Robotics and Automation (ICRA), Montréal, Canada, janvier 2019 Research team : AV

Large expert-curated database for benchmarking document similarity detection in biomedical literature search

Document recommendation systems for locating relevant literature have mostly relied on methods developed a decade ago. This is largely due to the lack of a large offline gold-standard benchmark of relevant documents that cover a variety of research fields such that newly developed literature search techniques can be compared, improved and translated into practice. To overcome this bottleneck, we have established the RElevant LIterature SearcH consortium consisting of more than 1500 scientists from 84 countries, who have collectively annotated the relevance of over 180 000 PubMed-listed articles with regard to their respective seed (input) article/s. The majority of annotations were contributed by highly experienced, original authors of the seed articles. The collected data cover 76% of all unique PubMed Medical Subject Headings descriptors. No systematic biases were observed across different experience levels, research fields or time spent on annotations. More importantly, annotations of the same document pairs contributed by different scientists were highly concordant. We further show that the three representative baseline methods used to generate recommended articles for evaluation (Okapi Best Matching 25, Term Frequency-Inverse Document Frequency and PubMed Related Articles) had similar overall performances. Additionally, we found that these methods each tend to produce distinct collections of recommended articles, suggesting that a hybrid method may be required to completely capture all relevant articles. The established database server located at https://relishdb.ict.griffith.edu.au is freely available for the downloading of annotation data and the blind testing of new methods. We expect that this benchmark will be useful for stimulating the development of new powerful techniques for title and title/abstract-based search engines for relevant articles in biomedical research.Peer reviewe

Autonomous guidewire navigation in a two dimensional vascular phantom

The treatment of cerebro- and cardiovascular diseases requires complex and challenging navigation of a catheter. Previous attempts to automate catheter navigation lack the ability to be generalizable. Methods of Deep Reinforcement Learning show promising results and may be the key to automate catheter navigation through the tortuous vascular tree. This work investigates Deep Reinforcement Learning for guidewire manipulation in a complex and rigid vascular model in 2D. The neural network trained by Deep Deterministic Policy Gradients with Hindsight Experience Replay performs well on the low-level control task, however the high-level control of the path planning must be improved further

Deep Reinforcement Learning for the Navigation of Neurovascular Catheters

Endovascular catheters are necessary for state-ofthe- art treatments of life-threatening and time-critical diseases like strokes and heart attacks. Navigating them through the vascular tree is a highly challenging task. We present our preliminary results for the autonomous control of a guidewire through a vessel phantom with the help of Deep Reinforcement Learning. We trained Deep-Q-Network (DQN) and Deep Deterministic Policy Gradient (DDPG) agents on a simulated vessel phantom and evaluated the training performance. We also investigated the effect of the two enhancements Hindsight Experience Replay (HER) and Human Demonstration (HD) on the training speed of our agents. The results show that the agents are capable of learning to navigate a guidewire from a random start point in the vessel phantom to a random goal. This is achieved with an average success rate of 86.5% for DQN and 89.6% for DDPG. The use of HER and HD significantly increases the training speed. The results are promising and future research should address more complex vessel phantoms and the use of a combination of guidewire and catheter

Deep Reinforcement Learning for the Navigation of Neurovascular Catheters

Endovascular catheters are necessary for state-of-the-art treatments of life-threatening and time-critical diseases like strokes and heart attacks. Navigating them through the vascular tree is a highly challenging task. We present our preliminary results for the autonomous control of a guidewire through a vessel phantom with the help of Deep Reinforcement Learning. We trained Deep-Q-Network (DQN) and Deep Deterministic Policy Gradient (DDPG) agents on a simulated vessel phantom and evaluated the training performance. We also investigated the effect of the two enhancements Hindsight Experience Replay (HER) and Human Demonstration (HD) on the training speed of our agents. The results show that the agents are capable of learning to navigate a guidewire from a random start point in the vessel phantom to a random goal. This is achieved with an average success rate of 86.5% for DQN and 89.6% for DDPG. The use of HER and HD significantly increases the training speed. The results are promising and future research should address more complex vessel phantoms and the use of a combination of guidewire and catheter

ALICE - Artificial Intelligence Catheter. Towards Autonomous Closed Loop Control of Passive Endovascular Catheters Based on Deep Reinforcement Learning: Poster presented at Emerging Learning Techniques for Robotics, Workshop at the Hamlyn Symposium on Medical Robotics, 26th June 2019, London

Endovascular catheters are used for state of the art therapies of many widespread diseases. Navigating them can be very laborious and so far no robotic assistance exists for passive catheters. Steer-able catheters exist, but due to their large diameter they are not suitable for many interventions. We propose a closed loop control system where a deep reinforcement learning based control algorithm steers the catheter. The algorithm is provided with live data by a tracking system. Prior to the intervention the control algorithm is trained on the simulation model and by expert demonstration. Here we present the results of our experiments, where a control algorithm learns to steer a guidewire through a simplified vascular tree. Learning is performed in the simulation model and the result transferred to the test bench. Our results show that the algorithm is able to learn catheter steering. However the simulation results cannot be transferred to the test bench directly without facing a reduced accuracy due to the test bench not having perfect states like the simulation