  Cardiovascular diseases (CVDs) are responsible for a large proportion of
premature deaths in low- and middle-income countries. Early CVD detection and
intervention is critical in these populations, yet many existing CVD risk
scores require a physical examination or lab measurements, which can be
challenging in such health systems due to limited accessibility. Here we
investigated the potential to use photoplethysmography (PPG), a sensing
technology available on most smartphones that can potentially enable
large-scale screening at low cost, for CVD risk prediction. We developed a deep
learning PPG-based CVD risk score (DLS) to predict the probability of having
major adverse cardiovascular events (MACE: non-fatal myocardial infarction,
stroke, and cardiovascular death) within ten years, given only age, sex,
smoking status and PPG as predictors. We compared the DLS with the office-based
refit-WHO score, which adopts the shared predictors from WHO and Globorisk
scores (age, sex, smoking status, height, weight and systolic blood pressure)
but refitted on the UK Biobank (UKB) cohort. In UKB cohort, DLS's C-statistic
(71.1%, 95% CI 69.9-72.4) was non-inferior to office-based refit-WHO score
(70.9%, 95% CI 69.7-72.2; non-inferiority margin of 2.5%, p<0.01). The
calibration of the DLS was satisfactory, with a 1.8% mean absolute calibration
error. Adding DLS features to the office-based score increased the C-statistic
by 1.0% (95% CI 0.6-1.4). DLS predicts ten-year MACE risk comparable with the
office-based refit-WHO score. It provides a proof-of-concept and suggests the
potential of a PPG-based approach strategies for community-based primary
prevention in resource-limited regions.

Computer Vision and Pattern Recognition;Artificial Intelligence;Machine Learning