Sujay Kakarmath
Sujay Kakarmath is a Product Manager at Google Research, specializing in health AI projects. With a background as a physician and experience in global public health and digital health research, Sujay focuses on translating AI advancements into healthcare innovations. His recent work includes predicting cardiovascular risk using smartphone signals and developing a bioacoustic model to identify biomarkers for respiratory conditions such as tuberculosis and childhood pneumonia
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Predicting Cardiovascular Disease Risk using Photoplethysmography and Deep Learning
Sebastien Baur
Christina Chen
Mariam Jabara
Babak Behsaz
Shravya Shetty
Goodarz Danaei
Diego Ardila
PLOS Glob Public Health, 4(6)(2024), e0003204
Preview abstract
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. 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 compare 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. All models were trained on a development dataset (141,509 participants) and evaluated on a geographically separate test (54,856 participants) dataset, both from UKB. DLS’s C-statistic (71.1%, 95% CI 69.9–72.4) is 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) in the test dataset. The calibration of the DLS is satisfactory, with a 1.8% mean absolute calibration error. Adding DLS features to the office-based score increases 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. Interpretability analyses suggest that the DLS-extracted features are related to PPG waveform morphology and are independent of heart rate. Our study provides a proof-of-concept and suggests the potential of a PPG-based approach strategies for community-based primary prevention in resource-limited regions.
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