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. The developed DNN model and related documentation in this project are available at GitHub page at https://github.com/pip-alireza/DeepCalcScoring.

翻译：动脉粥样硬化是一种影响大动脉的慢性炎症性疾病，已成为全球性健康风险。准确分析计算机断层血管造影（CTA）等诊断图像，对于外周动脉疾病（PAD）等动脉粥样硬化相关疾病的分期和进展监测至关重要。然而，人工分析CTA图像既耗时又繁琐。为解决这一局限，我们采用深度学习模型对接受股动脉内膜剥脱术的PAD患者CTA图像中的血管系统进行分割，并测量从左肾动脉至髌骨区域的血管钙化程度。利用Prisma Health Midlands提供的27例股动脉内膜剥脱术患者的专属CTA图像，我们开发了一个深度神经网络（DNN）模型：首先对从降主动脉至髌骨的动脉系统进行分割，其次提供动脉钙化量化指标。该DNN模型在主动脉至髌骨段动脉分割中实现了83.4%的平均Dice精度，较现有最优方法提升0.8%。此外，本研究首次利用深度学习对下肢自动化钙化测量进行了稳健的统计分析，自动与人工钙化评分之间的平均绝对百分比误差（MAPE）为9.5%，相关系数达0.978。上述结果凸显了深度学习技术作为医疗专业人员快速准确评估腹主动脉及髌骨以上分支钙化程度的潜力。本研究所开发的DNN模型及相关文档已发布于GitHub页面（https://github.com/pip-alireza/DeepCalcScoring）。