Smartphone camera oximetry in an induced hypoxemia study
Jason S. Hoffman, Varun Viswanath, Caiwei Tian, Xinyi Ding, Matthew Thompson, Eric C. Larson, Shwetak Patel, Edward Jay Wang
Abstract
Abstract Hypoxemia, a medical condition that occurs when the blood is not carrying enough oxygen to adequately supply the tissues, is a leading indicator for dangerous complications of respiratory diseases like asthma, COPD, and COVID-19. While purpose-built pulse oximeters can provide accurate blood-oxygen saturation (SpO 2 ) readings that allow for diagnosis of hypoxemia, enabling this capability in unmodified smartphone cameras via a software update could give more people access to important information about their health. Towards this goal, we performed the first clinical development validation on a smartphone camera-based SpO 2 sensing system using a varied fraction of inspired oxygen (FiO 2 ) protocol, creating a clinically relevant validation dataset for solely smartphone-based contact PPG methods on a wider range of SpO 2 values (70–100%) than prior studies (85–100%). We built a deep learning model using this data to demonstrate an overall MAE = 5.00% SpO 2 while identifying positive cases of low SpO 2 < 90% with 81% sensitivity and 79% specificity. We also provide the data in open-source format, so that others may build on this work.