AI-based modality-agnostic classification system for vascular calcifications

The importance of vascular calcification in major adverse cardiovascular events such as heart attacks or strokes has been established. However, calcifications have heterogeneous phenotypes, and their influence on diseased tissue stability remains poorly understood. Precise classification of calcification phenotypes is therefore essential for determining their impact on tissue stability through clinical and basic science studies. Here, we introduce a new classification system for phenotyping calcification along with a semi-automatic, non-destructive pipeline that can distinguish these phenotypes in imaging datasets. This pipeline covers diverse calcification phenotypes characterized by their size-related, morphological, spatial, and environmental phenotypes. We demonstrated its applicability using high-resolution micro-CT images of five arterial and aneurysmal specimens. The pipeline comprises an annotation-efficient, semi-automatic deep learning-based segmentation framework for the segmentation of sample and lipid pools in large noisy μ-CT stacks, an in-house 3D reconstruction tool, and advanced unsupervised clustering techniques for calcification classifications. The segmentation framework achieved high accuracy, with mean Dilated Dice Similarity Coefficients (dilation radius: 2 pixels) of 0.998 ± 0.003 (95% CI 0.997-0.999) for sample segmentation and 0.961 ± 0.031 (95% CI 0.955-0.967) for lipid pool segmentation across all samples using only 13 manually marked slices for each stack. Relying on 3D models rather than input images makes our classification system applicable to any imaging technique allowing 3D reconstructions, such as micro-CT and micro-OCT. This provides a common language across studies to communicate findings on the role of each calcification phenotype and potentially paves the way toward identifying novel biomarkers for accurate cardiovascular risk assessment.