# Smartphone Camera Oximetry in an Induced Hypoxemia Study

arXiv: https://arxiv.org/abs/2104.00038 · HTML: https://mlsanity.com/paper/2104.00038/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 1 of its 53 references that are in the map.
- First posted on arXiv 31 Mar 2021.

## Builds on (most cited first, 1 of 1)

- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)

## Built on by (most cited first, 7 of 7)

- [Reliable and Trustworthy Machine Learning for Health Using Dataset Shift Detection](https://mlsanity.com/paper/2110.14019/) (2021)
- [Summit Vitals: Multi-Camera and Multi-Signal Biosensing at High Altitudes](https://mlsanity.com/paper/2409.19223/) (2024)
- [Tiny-PPG: A Lightweight Deep Neural Network for Real-Time Detection of Motion Artifacts in Photoplethysmogram Signals on Edge Devices](https://mlsanity.com/paper/2305.03308/) (2023)
- [Efficient Deep Learning-based Estimation of the Vital Signs on Smartphones](https://mlsanity.com/paper/2204.08989/) (2022)
- [M3PD Dataset: Dual-view Photoplethysmography (PPG) Using Front-and-rear Cameras of Smartphones in Lab and Clinical Settings](https://mlsanity.com/paper/2511.02349/) (2025)
- [You can monitor your hydration level using your smartphone camera](https://mlsanity.com/paper/2402.07467/) (2024)
- [An AI-Enabled Framework Within Reach for Enhancing Healthcare Sustainability and Fairness](https://mlsanity.com/paper/2406.07558/) (2024)

## Abstract (arXiv)

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, 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 SpO2 sensing system using a varied fraction of inspired oxygen (FiO2) protocol, creating a clinically relevant validation dataset for solely smartphone-based methods on a wide range of SpO2 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 SpO2 level with an overall MAE=5.00% SpO2 and identifying positive cases of low SpO2<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 FiO2 study in open-source format, so that others may build on this work.