Automated Measurement of Vascular Calcification in Femoral Endarterectomy Patients Using Deep Learning.

Atherosclerosis, a chronic inflammatory disease affecting the large arteries, presents a global health risk. Accurate analysis of diagnostic images, like computed tomographic angiograms (CTAs), is essential for staging and monitoring the progression of atherosclerosis-related conditions, including peripheral arterial disease (PAD). However, manual analysis of CTA images is time-consuming and tedious. To address this limitation, we employed a deep learning model to segment the vascular system in CTA images of PAD patients undergoing femoral endarterectomy surgery and to measure vascular calcification from the left renal artery to the patella. Utilizing proprietary CTA images of 27 patients undergoing femoral endarterectomy surgery provided by Prisma Health Midlands, we developed a Deep Neural Network (DNN) model to first segment the arterial system, starting from the descending aorta to the patella, and second, to provide a metric of arterial calcification. Our designed DNN achieved 83.4% average Dice accuracy in segmenting arteries from aorta to patella, advancing the state-of-the-art by 0.8%. Furthermore, our work is the first to present a robust statistical analysis of automated calcification measurement in the lower extremities using deep learning, attaining a Mean Absolute Percentage Error (MAPE) of 9.5% and a correlation coefficient of 0.978 between automated and manual calcification scores. These findings underscore the potential of deep learning techniques as a rapid and accurate tool for medical professionals to assess calcification in the abdominal aorta and its branches above the patella.

Novel Payloads to Mitigate Maladaptive Inward Arterial Remodeling in Drug-Coated Balloon Therapy.

Drug-coated balloon therapy is a minimally invasive endovascular approach to treat obstructive arterial disease, with increasing utilization in the peripheral circulation due to improved outcomes as compared to alternative interventional modalities. Broader clinical use of drug-coated balloons is limited by an incomplete understanding of device- and patient-specific determinants of treatment efficacy, including late outcomes that are mediated by postinterventional maladaptive inward arterial remodeling. To address this knowledge gap, we propose a predictive mathematical model of pressure-mediated femoral artery remodeling following drug-coated balloon deployment, with account of drug-based modulation of resident vascular cell phenotype and common patient comorbidities, namely, hypertension and endothelial cell dysfunction. Our results elucidate how postinterventional arterial remodeling outcomes are altered by the delivery of a traditional anti-proliferative drug, as well as by codelivery with an anti-contractile drug. Our findings suggest that codelivery of anti-proliferative and anti-contractile drugs could improve patient outcomes following drug-coated balloon therapy, motivating further consideration of novel payloads in next-generation devices.