Reduction of Pigmentation Dependent Errors using Spectroscopic Pulse Oximetery

项目总结/摘要 脉搏血氧仪是一种强有力的临床工具，用于心肺功能受损患者的临床评价， 然而，多项临床研究表明，脉搏血氧饱和度在黑暗中存在高估误差， 有色人种，这是筛查心肺疾病（包括COVID）的一个严重错误， 19，并可能有助于少数民族的结果差异的报告。Aimloxy，LLC一直在开发 高频脉冲传感技术（HF-PST）的专有硬件和固件，代表 下一代光电容积描记（PPG）脉冲传感技术的发展， 传统上用作脉搏血氧测定的支柱。光学诊断研究实验室（ODRL） 在坦普尔大学，参与了低资源环境下光学传感技术的开发 并开发了基于手机的技术，包括减少色素沉着的策略 非裔婴儿经皮胆红素水平检测的依赖性不准确性。ODRL，Aimloxy， 沿着的还有天普大学医院急诊科和福克斯学院的合作者 业务的数据分析中心将合作进行初始开发和台式 下一代高频光谱脉搏血氧仪的表征 PPG和血氧测定信号中的色素沉着依赖性误差，进行初始人类受试者可行性研究 在多种族人群中，并结合完善的机器学习算法，以改善测量 在有色人种中的准确性。第一阶段提案的顺利完成将为 II期全规模研究，以开发商业原型并进行多中心试验， 准确性，并与传统技术相比，在深色肤色的人，最终 帮助减少少数民族的医疗差距。

PROJECT SUMMARY/ ABSTRACT Pulse oximetry is a powerful clinical tool for clinical evaluation of patients with cardiopulmonary compromise, however, multiple clinical studies have indicated pulse oximetry suffers from overestimation errors in darkly pigmented individuals, which is a critical mistake in screening for a cardiopulmonary disease, including COVID- 19, and could contribute to reports of outcome disparities in ethnic minorities. Aimloxy, LLC has been developing proprietary hardware and firmware for High Frequency Pulse Sensing Technology (HF-PST), which represents a next-generation evolution of photoplethysmography (PPG) pulse sensing technologies, which have conventionally served as the backbone of pulse oximetry. The Optical Diagnostic Research Laboratory (ODRL) at Temple University is involved in the development of optical sensing technologies for low-resource settings and has developed mobile-phone based technologies that include strategies for reducing pigmentation dependent inaccuracy in transcutaneous sensing of bilirubin levels in African-origin infants. The ODRL, Aimloxy, along with collaborators from Temple University Hospital’s Department of Emergency Medicine and Fox School of Business’ Center for Data Analytics will team together to perform initial development and benchtop characterization of a next-generation high frequency spectroscopic pulse oximeter capable of reducing pigmentation dependent errors in PPG and oximetry signals, conduct an initial human subjects feasibility study on a multi-ethnic population, and incorporate refined machine learning algorithms to improve measurement accuracy in pigmented individuals. The successful completion of this phase I proposal will set the stage for a full-scale study in phase II to develop a commercial prototype and perform multicenter trials to characterize accuracy and compare performance against traditional techniques in darkly pigmented individuals, ultimately helping reduce healthcare disparities in racial minorities.