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.

翻译：缺血性血球是一种医学条件,当血液没有携带足够的氧气来充分供应组织时,即出现缺氧,是哮喘、COPD和COVID-19等呼吸系统疾病危险并发症的主要指标。尽管专门设计的脉冲血氧计能提供准确的血液-氧饱和度(SpO$_2美元)读数,从而能够诊断缺氧性贫血症,通过软件更新在未经改进的智能手机相机上建立这种能力,可以让更多的人获得有关其健康的重要信息,以及提高医生远程诊断和治疗呼吸状况的能力。在这项工作中,我们迈出了一步,在基于智能的手机SO$2的SO_2系统上进行了首次临床发展验证。我们用不同部分的灵感氧(FO$_2美元)协议,建立了与临床相关的验证数据集,在广泛范围为spO$-2美元(70%-100%)的智能手机上,与以前评估范围更小范围(85-100%)业绩的研究相比,我们为实现这一目标迈出了一步。我们用一种深层次的SBYO数据模型,用这个精确的SBIO格式,我们用这个精确的SO=81O数据来测量样本分析可以提供精确的SBRO案例。