This work has been submitted to the IEEE for possible publication. Abstract--In endovascular surgery, endovascular intervention-ists push a thin tube called a catheter, guided by a thin wire to a treatment site inside the patient's blood vessels to treat various conditions such as blood clots, aneurysms, and malformations. Guidewires with robotic tips can enhance maneuverability, but they present challenges in modeling and control. Automation of soft robotic guidewire navigation has the potential to overcome these challenges, increasing the precision and safety of endovascular navigation. In other surgical domains, end-to-end imitation learning has shown promising results. Thus, we develop a transformer-based imitation learning framework with goal conditioning, relative action outputs, and automatic contrast dye injections to enable generalizable soft robotic guidewire navigation in an aneurysm targeting task. We train the model on 36 different modular bifurcated geometries, generating 647 total demonstrations under simulated fluoroscopy, and evaluate it on three previously unseen vascular geometries. The model can autonomously drive the tip of the robot to the aneurysm location with a success rate of 83% on the unseen geometries, outperforming several baselines. In addition, we present ablation and baseline studies to evaluate the effectiveness of each design and data collection choice. IAGNOSIS and treatment of vascular conditions require an endovascular interventionist to skillfully advance catheters and guidewires through the patient's blood vessels.

To date, endovascular surgeries are performed using the golden standard of Fluoroscopy, which uses ionising radiation to visualise catheters and vasculature. Prolonged Fluoroscopic exposure is harmful for the patient and the clinician, and may lead to severe post-operative sequlae such as the development of cancer. Meanwhile, the use of interventional Ultrasound has gained popularity, due to its well-known benefits of small spatial footprint, fast data acquisition, and higher tissue contrast images. However, ultrasound images are hard to interpret, and it is difficult to localise vessels, catheters, and guidewires within them. This work proposes a solution using an adaptation of a state-of-the-art machine learning transformer architecture to detect and segment catheters in axial interventional Ultrasound image sequences. The network architecture was inspired by the Attention in Attention mechanism, temporal tracking networks, and introduced a novel 3D segmentation head that performs 3D deconvolution across time. In order to facilitate training of such deep learning networks, we introduce a new data synthesis pipeline that used physics-based catheter insertion simulations, along with a convolutional ray-casting ultrasound simulator to produce synthetic ultrasound images of endovascular interventions. The proposed method is validated on a hold-out validation dataset, thus demonstrated robustness to ultrasound noise and a wide range of scanning angles. It was also tested on data collected from silicon-based aorta phantoms, thus demonstrated its potential for translation from sim-to-real. This work represents a significant step towards safer and more efficient endovascular surgery using interventional ultrasound.

Scientists at the Max Planck Institute for Intelligent Systems in Stuttgart have developed a soft robotic tool that promises to one day transform minimally invasive endovascular surgery. The two-part magnetic tool can help to visualise in real time the fine morphological details of partial vascular blockages such as stenoses, even in the narrowest and most curved vessels. It can also find its way through severe blockages such as chronic total occlusions. This tool could one day take the perception of endovascular medical devices a step further. Intravascular imaging techniques and microcatheter procedures are becoming ever more advanced, revolutionizing the diagnosis and treatment of many diseases.

Artificial intelligence (AI) is improving surgical planning, guidance and review, says Paul Mussenden, Chief Executive Officer, Cydar Medical. It's operating in all areas of healthcare and helping join up the different stages of the care pathway. That's because AI is very good at rationalising lots of complex data in a broad range of areas such as imaging data, diagnostic data, clinical data and genetic data -- and using it to personalise healthcare for individual patients. It gives clinicians the best information and new insights to make better decisions. Over the last 15 years, there has been a big shift to minimally invasive procedures.