Vascular calcification is implicated as an important factor in major adverse
cardiovascular events (MACE), including heart attack and stroke. A controversy
remains over how to integrate the diverse forms of vascular calcification into
clinical risk assessment tools. Even the commonly used calcium score for
coronary arteries, which assumes risk scales positively with total
calcification, has important inconsistencies. Fundamental studies are needed to
determine how risk is influenced by the diverse calcification phenotypes.
However, studies of these kinds are hindered by the lack of high-throughput,
objective, and non-destructive tools for classifying calcification in imaging
data sets. Here, we introduce a new classification system for phenotyping
calcification along with a semi-automated, non-destructive pipeline that can
distinguish these phenotypes in even atherosclerotic tissues. The pipeline
includes a deep-learning-based framework for segmenting lipid pools in noisy
micro-CT images and an unsupervised clustering framework for categorizing
calcification based on size, clustering, and topology. This approach is
illustrated for five vascular specimens, providing phenotyping for thousands of
calcification particles across as many as 3200 images in less than seven hours.
Average Dice Similarity Coefficients of 0.96 and 0.87 could be achieved for
tissue and lipid pool, respectively, with training and validation needed on
only 13 images despite the high heterogeneity in these tissues. By introducing
an efficient and comprehensive approach to phenotyping calcification, this work
enables large-scale studies to identify a more reliable indicator of the risk
of cardiovascular events, a leading cause of global mortality and morbidity.

血管钙化是心脏病发作和中风等重大不良心血管事件的重要因素。本研究提出了一种用于测定钙化表型的新的分类系统，以及一种半自动、无损的钙化分类工具，能够在动脉粥样硬化组织中区分这些表型。通过引入高效全面的钙化表型分类方法，本研究使得大规模研究能够识别心血管事件的更可靠风险指标。

利用人工智能对血管组织中的钙化进行表型化

Phenotyping calcification in vascular tissues using artificial  intelligence

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 this https URL

动脉粥样硬化是一种影响大动脉的慢性炎症性疾病；使用深度学习模型分割 CTA 图像中的血管系统以及计算血管钙化程度的 Metric，可提供医学专业人员对腹主动脉和膝下的钙化进行快速准确的评估。