  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, as well as
improve physicians' ability to remotely diagnose and treat respiratory
conditions. In this work, we take a step towards this goal by performing the
first clinical development validation on a smartphone-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 methods on
a wide range of SpO$_2$ values (70%-100%) for the first time. This contrasts
with previous studies, which evaluated performance on a far smaller range
(85%-100%). We build a deep learning model using this data to demonstrate
accurate reporting of SpO$_2$ level with an overall MAE=5.00% SpO$_2$ and
identifying positive cases of low SpO$_2$<90% with 81% sensitivity and 79%
specificity. We ground our analysis with a summary of recent literature in
smartphone-based SpO2 monitoring, and we provide the data from the FiO$_2$
study in open-source format, so that others may build on this work.
