A cloud-based artificial intelligence system fuses a patient’s pre-operative CT scan with live X-ray images during keyhole surgery on the aorta, giving surgeons a 3D overlay of soft tissues that standard 2D X-ray alone cannot show. X-ray fluoroscopy is the workhorse of minimally invasive surgery, but it sees soft tissue poorly. This forces surgeons to rely on contrast dye injections and guesswork, leading to longer procedures, higher radiation doses for patients and staff, and inconsistent outcomes. The trial tests whether Cydar EV—a device that uses computer vision to align CT and X-ray data in real time—can fix that. If the trial succeeds, the immediate impact is practical: shorter operations, less radiation, and more predictable results for the 322 patients undergoing endovascular aortic repair. But the bigger shift is in how hospitals buy imaging technology. Cydar EV runs as a cloud subscription, replacing the need for expensive capital purchases of advanced imaging hardware. That could make precision guidance available to more hospitals, and the same cloud-AI approach could later be adapted for other X-ray-guided procedures, from stenting to tumour ablation.
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Research question Can a novel type of medical device comprised of real-time cloud computing, AI and computer vision, improve the clinical and cost-effectiveness of X-ray guided surgery? Background X-ray fluoroscopy-guided surgery is a large and growing segment of the minimally-invasive surgery market, but is limited by 2D imaging that visualises soft tissues poorly. This poor visualisation contributes to imprecision and variable patient outcomes. Cydar EV uses computer vision to augment X-ray fluoroscopy imaging by fusing it with 3D soft tissue information from the patient s diagnostic CT scan with high accuracy and robustness. Pilot data from commercial test sites have shown Cydar EV is associated with reduced operating times and lower radiation exposure. Aims and Objectives This trial seeks to establish strong clinical evidence that Cydar EV reduces procedure duration, radiation exposure of patients and staff and is cost-effective. Methods Multi-centre, two-armed, randomised controlled trial of 322 patients with abdominal and/or thoraco-abdominal aortic disease undergoing endovascular repair using standard X-ray fluoroscopy imaging alone or augmented with Cydar-EV image fusion. The primary outcome measure is procedure time, and secondary outcome measures include radiation exposure, quality of life and technical success. Timelines for Delivery Open ten sites by months 9; recruit all patients by month 20; follow-up data by month 31; health economic analysis and submission of clinical, technical and cost-effectiveness data before end of month 36. Anticipated impact and dissemination Cydar EV is pioneering a new class of cloud digital products that promise to revolutionise the precision and consistency of patient outcomes. As a value-based subscription, they offer an alternative to high capital-expenditure procurement of imaging capabilities and clinical data insights. Establishing strong clinical evidence would accelerate adoption of Cydar EV for the benefit of patients, hospitals, and health services globally, as well as stimulating development of future products in this class.
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