Smartphone camera oximetry in an induced hypoxemia study

Jason S Hoffman; Varun K Viswanath; Caiwei Tian; Xinyi Ding; Matthew J Thompson; Eric C Larson; Shwetak N Patel; Edward J Wang

doi:10.1038/s41746-022-00665-y

Smartphone camera oximetry in an induced hypoxemia study

NPJ Digit Med. 2022 Sep 19;5(1):146. doi: 10.1038/s41746-022-00665-y.

Authors

Jason S Hoffman^#¹, Varun K Viswanath^#^{2

3}, Caiwei Tian⁴, Xinyi Ding⁵, Matthew J Thompson⁶, Eric C Larson⁵, Shwetak N Patel^{4

7}, Edward J Wang^{2

3}

Affiliations

¹ Paul G. Allen School of Computer Science and Engineering, University of Washington, Seattle, WA, USA. jasonhof@cs.washington.edu.
² Department of Electrical and Computer Engineering, University of California San Diego, La Jolla, CA, USA.
³ The Design Lab, University of California San Diego, La Jolla, CA, USA.
⁴ Paul G. Allen School of Computer Science and Engineering, University of Washington, Seattle, WA, USA.
⁵ Department of Computer Science, Southern Methodist University, Dallas, TX, USA.
⁶ Department of Family Medicine, University of Washington, Seattle, WA, USA.
⁷ Department of Electrical and Computer Engineering, University of Washington, Seattle, WA, USA.

^# Contributed equally.

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 (SpO₂) 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 SpO₂ sensing system using a varied fraction of inspired oxygen (FiO₂) protocol, creating a clinically relevant validation dataset for solely smartphone-based contact PPG methods on a wider range of SpO₂ values (70-100%) than prior studies (85-100%). We built a deep learning model using this data to demonstrate an overall MAE = 5.00% SpO₂ while identifying positive cases of low SpO₂ < 90% with 81% sensitivity and 79% specificity. We also provide the data in open-source format, so that others may build on this work.