AngioCloud & Brain Aneurysms
AngioCloud: the Interface of Software, Strategy, and Skill
Each day, in the U. S. there is a brain aneurysm every 18 minutes. The symptoms are often subtle and include localized headaches and blurred vision resulting in a survival rate of sixty to seventy percent. Unfortunately, half of those who survive current treatments will experience neurological deficits and complications.
Treating an aneurysm before it ruptures requires time efficiency and high levels of accuracy. In order to prevent rupture, surgeons use a device called a flow diverter, or stent, to target the aneurysm, but this device requires immense precision in an unpredictable environment. Each brain is unique and the location of each aneurysm changes, which requires highly detailed, personalized deployment of the device. The size of the stent is a key aspect of its success, yet there is little aggregate data to properly advise surgeons and they rely on making educated guesses using rudimentary data in order to use the proper size. This gap in treatment leaves physicians in a constant guessing game, puts patient lives at risk, and can increase waste of highly valued, specialized stents.
This issue served as the catalyst for a team of Emory researchers to create software based in shared knowledge: AngioCloud. Math and Computer Science postdoctoral fellow Leandro Gryngarten played a role in the interdisciplinary team that brought this technology to life, which required expertise of medical practitioners as well as researchers. “The development of AngioCloud is a great example of collaboration demonstrating the benefits of clinicians working with software engineers to design a superior product that meets the needs of the practicing physician”, states Hyeon (Sean) Kim, licensing assocaite in the Office of Technology Transfer. AngioCloud not only includes high quality imaging to map the individual brain curvature and the ideal size of the stent and deployment strategy, but it also takes the information and creates a shared database.
This shared database is able to process the high level information of rotational angiography (RA), computed tomography (CT), and magnetic resonance (MR) that traditional software cannot handle. Beyond the remote software’s processing ability, its cloud sharing system can improve results and mapping over time through analyses of the information it collects. Gordon described the technology’s potential to increase accessibility, and as the accessibility increases, the better the tool will become. AngioCloud brings together a number of disciplines, including geometry, statistics, and engineering tools, to calculate a flow diverter size that is accurate within 2.9mm compared with the current human error of 7.6mm.
The web-based platform has potential far beyond its applications to the treatment of brain aneurysm; it can create a system of sharing to improvement treatment and care outcomes of numerous surgeries. Although the medical world has been slow to adopt cloud-sharing technology, AngioCloud provides software that combines high quality imaging, personalized mapping, shared data, and, most importantly, improved care outcomes.