Artificial Intelligence for Thoracic Aortic Disease Screening and Gene Discovery

About This Grant

PROJECT SUMMARY Thoracic aortic dissections are a leading cause of premature death, especially in those with heritable thoracic aortic disease (HTAD) who carry pathogenic variants (PVs) in genes predisposing to dissection. Early HTAD diagnosis, surveillance of aneurysms, and elective surgical repair can effectively prevent aortic dissections. Unfortunately, many individuals go undiagnosed and consequently fail to receive early medical interventions and preventive surgery, resulting in death in up to 50% of dissection cases, including in childhood. Therefore, improved screening methods for HTAD are essential, particularly in children and cases where syndromic features are missed. While genetic testing has become integral to diagnosing HTAD, these genes explain less than 30% of cases. Thus, there is a pressing need for improved screening methods and a deeper understanding of the genetic causes of HTAD – areas where Artificial Intelligence (AI) can make significant contributions. In this proposal, Dr. Murdock proposes developing a state-of-the-art AI-based facial recognition tool specifically designed for HTAD using facial images from the Montalcino Aortic Consortium (MAC), an international patient registry of over 1,500 individuals with genetically mediated aortic disease. Preliminary results on vascular Ehlers- Danlos syndrome (vEDS) illustrate that this cutting-edge approach identifies vEDS accurately and distinguishes it from hypermobile EDS (hEDS), a larger patient group that does not have deadly vascular complications. This screening method could rapidly identify patients with potential PVs in HTAD genes, facilitating early detection from the pediatrician's office to emergency room settings. Additionally, Dr. Murdock proposes using machine learning and quantitative evolutionary information to identify new HTAD genes from large, diverse groups, including local HTAD cohorts, the UK Biobank, All of Us, and the Penn Medicine BioBank. Exciting preliminary findings using this approach on the UK Biobank dataset suggest the potential role of a novel myosin heavy chain gene, MYH9, in aortic dissections. The proposed work will take place at The University of Texas Health Science Center at Houston (UTHealth) in the Division of Medical Genetics within the Department of Internal Medicine. Dr. Murdock will conduct his research under the mentorship of Dr. Dianna Milewicz, a world-renowned expert in aortic disease, and an impressive advisory team of experts in cardiovascular genetics (Dr. Siddharth Prakash), artificial intelligence (Dr. Xiaoqian Jiang), computational genomic analysis (Dr. Olivier Lichtarge), and biostatistics (Dr. Cici Bauer). Dr. Murdock aims to become an NIH-funded independent investigator, leveraging his clinical, molecular, and computational background to improve the outcomes of individuals at risk for thoracic aortic disease. By receiving additional mentorship and training through the K08 award, he is confident he will acquire the necessary skills to transition to independence successfully.