An Unsupervised Learning Algorithm for the Automatic Classification of Coronary Artery Lesions

Background Coronary artery disease (CAD) remains a leading cause of mortality globally. Accurate identification and characterization of significant coronary artery lesions via CT is important for proper diagnosis and patient management. However, current supervised techniques for lesion identification require detailed manual annotations, which are both labor-intensive and prone to human error. While semi-supervised or weakly supervised approaches can partially reduce manual annotation burden, they still depend on annotated datasets, which remain limited and inconsistent in CAD research. Therefore, there is a strong clinical motivation for developing robust unsupervised methods that eliminate annotation dependency. Aim This study aims to develop and evaluate a novel unsupervised method utilizing clustering algorithms to automatically classify and characterize significant lesions in coronary arteries from CT images, thereby addressing limitations of supervised and semi-supervised methods. Methods We analyzed 45 anonymized coronary artery CT scans from patients hospitalized between 2018 and 2022, selected based on the presence of plaques causing at least 30% stenosis. Although small, the dataset represented a clinically relevant population with a diverse range of lesion types (calcified, mixed, and soft plaques). Vessel segmentation was performed using nnU-Net, followed by skeletonization and extraction of statistical and Haralick texture features. Dimensionality reduction was executed using principal component analysis, and lesion clustering was conducted using both k-means and a hybrid clustering algorithm. Supervised methods are defined as algorithms that require labeled data for training, whereas unsupervised methods, as applied in this study, do not require labeled data and instead rely solely on inherent patterns within the imaging features. The effectiveness of lesion classification, including calcified, mixed, and soft plaques, was assessed. Additionally, hemodynamic significance was verified by comparison with fractional flow reserve (FFR) measurements. Results Vessel segmentation yielded a mean Dice coefficient of 0.93, indicating high segmentation accuracy. The hybrid clustering algorithm demonstrated superior lesion classification performance, achieving sensitivity rates of 95.6% for calcified plaques, 88.3% for mixed plaques, and 74.1% for soft plaques. These performance indicators compare favorably to previously reported supervised and unsupervised approaches. Furthermore, the method reliably identified hemodynamically significant lesions as confirmed by FFR (n = 15 lesions). Conclusions Our proposed unsupervised clustering-based method effectively classifies and characterizes coronary artery lesions without the need for manual annotations. However, the small sample size and limited number of lesions validated by FFR (n = 15) restrict broad generalizations and clinical translation. External validation on larger, multicenter datasets is essential to confirm these promising findings. This method offers a practical, accurate, and efficient diagnostic approach, potentially streamlining clinical workflow and improving patient outcomes.