SUMMARY - ABSTRACT Thoracic aortic aneurysm is a highly prevalent disease which can lead to devastating complications including dissection or rupture. Early detection and regular monitoring of these patients via regular surveillance imaging is essential to guide therapy management. The current paradigm for risk assessment in these patients is based on primitive size thresholds with poor predictive value for aortic complications. There is strong evidence that 4D hemodynamic biomarkers are drivers of aortic complications and can improve risk assessment and therapy management. To obtain these biomarkers, a highly specialized MRI technique - 4D flow MRI - is required which allows for the direct in vivo measurement of aorta 4D hemodynamics. However, several limitations impede wider clinical translation, including the lack of access to dedicated MRI systems and 4D flow MRI protocols, burdensome and time-consuming (30+ minutes) post-processing, and interpretation of 4D flow data requiring dedicated software and highly specialized expertise. To address these limitations, Third Coast Dynamics is developing a cloud-based artificial intelligence (AI)-based platform called TCDflow that can replace 4D flow MRI by providing 4D hemodynamic output directly from widely available routine and easy-to-obtain clinical anatomic images of the chest. Our proof-of-concept studies have leveraged a large database of >6700 existing 4D flow MRI patient data to develop a prototype TCDflow fluid physics informed deep learning neural network for the prediction of 4D aortic hemodynamics using anatomic images as input data. Further development, evidence generation, and steps toward commercialization will be conducted in a two-phase approach. Phase 1 (P1) focuses on further development and fine-tuning of the This Coast Dynamics analysis pipeline (P1, Aim 1). The technology will then be tested in a large, single center (Northwestern) retrospective aorta outcomes study (P1, Aim 2). These developments and validation will provide the foundation for Phase 2 which focuses on developing our clinician-facing cloud-based analysis platform and report (P2, Aim 1), performing a large multicenter retrospective validation and outcomes study (P2, Aim 2), completing an end-user TCDflow evaluation (P2, Aim 3), and securing FDA 510(k) clearance (P2, Aim 4). The completion of the Phase 1 and 2 deliverables will result in an FDA-cleared product which can be readily commercialized. All aims are designed with guidance from consultants with direct expertise in FDA 510(k) clearance of digital health products. The technology will provide improved personalized risk-stratification of aortic complications beyond the current simple and insufficient clinical measures. Increasing operational efficiencies and reduced health care utilization costs will be achieved by access to cloud-based 4D hemodynamic assessment by a wide range of patients and healthcare providers without the need for highly specialized imaging equipment, training, and expertise.

PROJECT NARRATIVE The goal of this proposal is to develop a cloud-based artificial intelligence-based platform that will provide 4D hemodynamic output directly from widely available routine clinical and easy-to-obtain anatomic images of the chest. The technology will enable improved and personalized risk-stratification of complications in patients with aortic disease beyond the current simple and insufficient clinical measures. In addition, the technique can improve operational efficiencies and reduced health care utilization costs for a wide range of patients and healthcare providers without the need for highly specialized imaging equipment, training, and expertise.