Park Sang Mok, Visbal-Onufrak Michelle A, Haque Md Munirul, Were Martin C, Naanyu Violet, Hasan Md Kamrul, Kim Young L
Weldon School of Biomedical Engineering, Purdue University, West Lafayette, Indiana 47907, USA.
R. B. Annis School of Engineering, University of Indianapolis, Indianapolis, Indiana 46227, USA.
Optica. 2020 Jun 20;7(6):563-573. doi: 10.1364/optica.390409.
Although blood hemoglobin (Hgb) testing is a routine procedure in a variety of clinical situations, noninvasive, continuous, and real-time blood Hgb measurements are still challenging. Optical spectroscopy can offer noninvasive blood Hgb quantification, but requires bulky optical components that intrinsically limit the development of mobile health (mHealth) technologies. Here, we report spectral super-resolution (SSR) spectroscopy that virtually transforms the built-in camera (RGB sensor) of a smartphone into a hyperspectral imager for accurate and precise blood Hgb analyses. Statistical learning of SSR enables us to reconstruct detailed spectra from three color RGB data. Peripheral tissue imaging with a mobile application is further combined to compute exact blood Hgb content without personalized calibration. Measurements over a wide range of blood Hgb values show reliable performance of SSR blood Hgb quantification. Given that SSR does not require additional hardware accessories, the mobility, simplicity, and affordability of conventional smartphones support the idea that SSR blood Hgb measurements can be used as an mHealth method.
尽管血红蛋白（Hgb）检测在各种临床情况下都是常规程序，但无创、连续和实时的血液Hgb测量仍然具有挑战性。光谱学可以提供无创血液Hgb定量，但需要庞大的光学组件，这从本质上限制了移动健康（mHealth）技术的发展。在此，我们报告了光谱超分辨率（SSR）光谱学，它实际上将智能手机的内置摄像头（RGB传感器）转变为用于精确血液Hgb分析的高光谱成像仪。SSR的统计学习使我们能够从三色RGB数据重建详细光谱。通过移动应用程序进行外周组织成像，并进一步结合起来计算精确的血液Hgb含量，而无需个性化校准。在广泛的血液Hgb值范围内进行的测量显示了SSR血液Hgb定量的可靠性能。鉴于SSR不需要额外的硬件附件，传统智能手机的移动性、简单性和可承受性支持了SSR血液Hgb测量可作为一种mHealth方法使用的观点。
