Researchers developed a machine learning model that detects peripheral artery disease (PAD) by analyzing photoplethysmography (PPG) signals from toe pulses. The approach achieved 84.5% accuracy when combined with clinical data and demonstrated equitable performance across demographic groups. Original paper: Development and validation of a digital biomarker for peripheral artery disease. — NPJ digital medicine. 10.1038/s41746-026-02655-w 📄 Read the article