Aortic Valve Disease Detection from PPG via Physiology-Informed Self-Supervised Learning

TL;DR

This paper introduces a physiology-informed self-supervised learning framework that leverages large unlabeled PPG data to effectively screen for aortic valve diseases, outperforming traditional supervised methods.

Contribution

It presents a novel PG-SSL paradigm that incorporates clinical knowledge into self-supervised learning for PPG-based disease screening, addressing label scarcity.

Findings

Achieved AUCs of 0.765 for AS and 0.776 for AR screening.

Outperformed supervised models trained on limited labeled data.

Validated as an independent digital biomarker with prognostic value.

Abstract

Traditional diagnosis of aortic valve disease relies on echocardiography, but its cost and required expertise limit its use in large-scale early screening. Photoplethysmography (PPG) has emerged as a promising screening modality due to its widespread availability in wearable devices and its ability to reflect underlying hemodynamic dynamics. However, the extreme scarcity of gold-standard labeled PPG data severely constrains the effectiveness of data-driven approaches. To address this challenge, we propose and validate a new paradigm, Physiology-Guided Self-Supervised Learning (PG-SSL), aimed at unlocking the value of large-scale unlabeled PPG data for efficient screening of Aortic Stenosis (AS) and Aortic Regurgitation (AR). Using over 170,000 unlabeled PPG samples from the UK Biobank, we formalize clinical knowledge into a set of PPG morphological phenotypes and construct a pulse pattern recognition proxy task for self-supervised pre-training. A dual-branch, gated-fusion architecture is then employed for efficient fine-tuning on a small labeled subset. The proposed PG-SSL framework achieves AUCs of 0.765 and 0.776 for AS and AR screening, respectively, significantly outperforming supervised baselines trained on limited labeled data. Multivariable analysis further validates the model output as an independent digital biomarker with sustained prognostic value after adjustment for standard clinical risk factors. This study demonstrates that PG-SSL provides an effective, domain knowledge-driven solution to label scarcity in medical artificial intelligence and shows strong potential for enabling low-cost, large-scale early screening of aortic valve disease.