Comput Methods Programs Biomed. 2026 Jun 9;285:109508. doi: 10.1016/j.cmpb.2026.109508. Online ahead of print.
ABSTRACT
BACKGROUND AND OBJECTIVE: Early identification of carotid atherosclerosis (CAS) is critical for preventing cardio-cerebrovascular diseases. Mainstream screening methods (e.g. ultrasound, CTA) are operator-dependent and high cost. This study aimed to propose a novel non-contact facial imaging photoplethysmography (iPPG) approach for CAS risk assessment.
METHODS: A total of 95 middle-aged and elderly participants were enrolled, with synchronous facial iPPG signals and carotid/lower-extremity ultrasound data collected. A deep learning-based Period-aware Autoencoder (PA-AE) with bidirectional cross-modal attention was developed to reconstruct high-fidelity iPPG signals with periodic peak constraint and full-face reference signal fusion for robust noise suppression. Facial hemodynamic heatmaps were generated via signal-to-spatial mapping, interquartile range-based outlier removal, and spatial proximity repair. We analyzed the association between heatmap patterns and atherosclerosis using Pearson chi-square tests and Odds Ratios (OR).
RESULTS: The PA-AE outperformed traditional wavelet and LSTM-AE methods in signal periodicity preservation and noise reduction. The Type 3 facial iPPG heatmap (characterized by ≤ 20% red area distributed in the facial periphery) was significantly associated with carotid atherosclerosis (P=0.048), whereas no association was observed for lower extremity atherosclerosis (P=0.674). After adjusting for age, BMI, and hypertension in multivariable logistic regression, heatmap Type 3 still showed a positive trend with CAS (adjusted OR=2.29, 95%CI: 0.56-9.41), and robust statistical analyses including stratified analyses (age < 65 and non-hypertensive subgroups), ridge regression, and continuous red-area ratio quantification consistently confirmed this significant association.
CONCLUSIONS: Facial iPPG heatmaps, enhanced by the PA-AE, demonstrate significant potential as a non-invasive tool for identifying CAS risk, offering a promising avenue for accessible community healthcare screening.
PMID:42365708 | DOI:10.1016/j.cmpb.2026.109508