Automated Deep Learning-Based Detection of Early Atherosclerotic Plaques in Carotid Ultrasound Imaging

Abstract

Background: Carotid plaque presence is associated with cardiovascular risk, even among asymptomatic individuals. While deep learning has shown promise for carotid plaque phenotyping in patients with advanced atherosclerosis, its application in population-based settings of asymptomatic individuals remains unexplored.

Methods: We developed a YOLOv8-based model for plaque detection using carotid ultrasound images from 19,499 participants of the population-based UK Biobank (UKB) and fine-tuned it for external validation in the BiDirect study (N = 2,105). Cox regression was used to estimate the impact of plaque presence and count on major cardiovascular events. To explore the genetic architecture of carotid atherosclerosis, we conducted a genome-wide association study (GWAS) meta-analysis of the UKB and CHARGE cohorts. Mendelian randomization (MR) assessed the effect of genetic predisposition to vascular risk factors on carotid atherosclerosis.

Results: Our model demonstrated high performance with accuracy, sensitivity, and specificity exceeding 85%, enabling identification of carotid plaques in 45% of the UKB population (aged 47-83 years). In the external BiDirect cohort, a fine-tuned model achieved 86% accuracy, 78% sensitivity, and 90% specificity. Plaque presence and count were associated with risk of major adverse cardiovascular events (MACE) over a follow-up of up to seven years, improving risk reclassification beyond the Pooled Cohort Equations. A GWAS meta-analysis of carotid plaques uncovered two novel genomic loci, with downstream analyses implicating targets of investigational drugs in advanced clinical development. Observational and MR analyses showed associations between smoking, LDL cholesterol, hypertension, and odds of carotid atherosclerosis.

Conclusions: Our model offers a scalable solution for early carotid plaque detection, potentially enabling automated screening in asymptomatic individuals and improving plaque phenotyping in population-based cohorts. This approach could advance large-scale atherosclerosis research.

Keywords: atherosclerosis; cardiovascular disease; carotid artery; genetics; machine learning; vascular ultrasound.

Figure 1.. Development and performance of the plaque detection model.

A. Confusion matrix for model’s classification performance for plaque presence at each image. The confusion matrix is based on comparing the presence of plaque in the annotations with the model’s predictions. Specifically, if there is a plaque annotation for an image and the prediction contains a bounding box, then the prediction is annotated as true positive. B. Examples of model predictions as compared to manual annotations for three UK Biobank participants in the test set. Each image depicts the longitudinal view of the common carotid artery extending toward the bifurcation area (the left part of the images).

Figure 2.. Prevalence and predictors of carotid plaque in the UK Biobank.

A. Distribution of plaques in the left and right carotid arteries. B. Percentage of plaque presence across age and sex groups. Error bars represent 95% confidence intervals. C. Forest plot of the associations of demographic and vascular risk factors with the odds of carotid plaque presence, as derived by a multivariable logistic regression model that includes all variables in the figure. The results are presented as odds ratios (OR) and 95% confidence intervals (CI). SBP – systolic blood pressure; CVD – cardiovascular disease; LDL – low-density lipoprotein cholesterol; SD – standard deviation. * History of hypertension is defined by the use of antihypertensive medication

Figure 3.. Performance of the fine-tuned plaque detection model in the BiDirect sample.

A. Confusion matrix for model’s classification performance for plaque presence at each image. B. Examples of model predictions as compared to manual annotations for four BiDirect participants in the test set. C. Distribution of plaques in the left and right carotid arteries. D. Percentage of carotid plaque across age and sex groups. Error bars represent 95% confidence intervals.

Figure 4.. Survival curves of cumulative major adverse cardiovascular event (MACE) rates by total count of carotid plaques predicted by the model.

The presented hazard ratios (HRs) were estimated using Cox regression, adjusted for: sex, age, systolic blood pressure, use of statins, history of antihypertensive therapy, current smoking, history of diabetes, HDL cholesterol and total cholesterol levels.

Figure 5.. Manhattan plot of the GWAS meta-analysis for carotid plaque presence (29,790 cases; 36,847 controls).

Loci highlighted in red point to novel significant associations for carotid plaque whereas loci highlighted in green represent validation of previously described associations.

Figure 6.. Forest plot for Mendelian randomization results.

The results are presented as odds ratios (OR) and 95% confidence intervals (CI) derived from random-effects inverse-variance weighted Mendelian randomization analyses. Two-fold increments in prevalence for binary exposures (type 2 diabetes and smoking initiation) were derived by multiplying the IVW betas and corresponding confidence intervals by 0.693, as described by Burgess and Labrecque. HbA1c – Glycated hemoglobin; BMI – Body Mass Index; LDL – Low-Density Lipoprotein Cholesterol; HDL – High-Density Lipoprotein Cholesterol; SBP – Systolic Blood Pressure; DBP – Diastolic Blood Pressure; IL-6 – Interleukin-6.