The University of Florida’s MIRTH AI Lab recently received $5,000 for their paper submission entitled, “Volumetric Analysis of Uncompiled Type B Aortic Dissection Using an Automated Deep Learning Aortic Zone Segmentation Model.” Prized the winning paper by the Southern Association for Vascular Surgery (SAVS), Dr. Jonathan R Krebs, a General Surgery Resident with a deep interest in Vascular Surgery, eagerly awaits the presentation of his papers’ findings at the widely esteemed journal’s conference (SAVS 48th Annual Meeting) in January of 2025.
The award-winning paper aims to address the lack of expertise we have pertaining to the efficient diagnosis and treatment of acute uncomplicated Type B Aortic Dissections (auTBAD). This feat is proposed to be overcome via the implementation of deep learning techniques to accurately predict the progression of aortic growths. Historically, deep learning applications have shown excellent performance in three dimensional (3D) medical image analysis tasks, however they have not been recruited for TBAD applications, nor have they incorporated aortic zones defined by the Society for Vascular Surgery (SVS) or the Society for Vascular Surgeons (STS).
With this in mind, Co-Principal Investigators, Dr. Wei Shao, Assistant Professor of Quantitative Health in the Department of Medicine, Division of Nephrology, and Dr. Muhammad Imran, Postdoctoral Associate in the Department of Medicine, Division of Nephrology approach this matter from the more technical side. With the award, they hope to delve deeper into the refinement of deep learning approaches to medical imaging analyses and furthermore designate a reliable protocol for the treatment of auTBAD utilizing Artificial Intelligence (AI).