STTR Phase I: An AI-Enhanced Angiographic System to Guide Endovascular Treatment of Intracranial Aneurysms

Award ID: 2111865

The broader impact of this Small Business Technology Transfer (STTR) Phase I project falls within the larger scope of expanding artificial intelligence (AI) methods into health care applications. The application is focused on image guided endovascular surgical procedures for intracranial aneurysms (IA), which may cause subarachnoid hemorrhage, the most devastating type of hemorrhagic stroke. The current trends in treatment of aneurysms show that endovascular approach has become the mainstay procedure due to reduced surgical complications when compared with open skull surgery. Despite tremendous technological advances in devices and surgical instrumentation, as many as 30% of these lesions are not completely healed after the first surgical intervention, exposing patients to additional risks for complications due to multiple surgical procedures. The AI autonomous solution developed in this project will be the one of the first applications that provides intraoperative prognosis for six-month healing of an aneurysm after each surgical step to allow surgical adjustments, reducing the risk for ruptures and re-treatments from 30% to an estimated 5% and creating savings for the $65,000 in retreatments (roughly $1.95 B annually in the U.S.). This Small Business Technology Transfer (STTR) Phase I project will aim to develop a comprehensive and autonomous AI method that will provide intraoperative prognosis of complete healing for an IA at six months. In current clinical practice, neuro-interventionalists cannot guarantee successful healing of intracranial aneurysms immediately post-device placement. Treated patients have to wait a minimum of 3-6 months before their aneurysm is reassessed on medical imaging and the clinician decides if re-treatment is needed. During this critical time, patients are still at risk of rupture. In addition, re-treatments have higher risk to the patient as well as bear a financial burden on hospitals and insurance companies. The proposed algorithms will be fully integrated with surgical equipment and will allow dynamic angiographic analysis to derive physics-based parameters related to the nature of blood flow inside the aneurysm sac. These parameters are combined with a machine learning algorithm to provide a prediction as to whether the treatment is sufficient for a full healing. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Program Director: Peter Atherton