Mr. Sang Mok Park Profile

Sang Mok Park

at Purdue Univ

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Publications (2)

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Proceedings Article | 5 March 2021 Presentation + Paper

mHealth spectroscopy for noninvasive blood hemoglobin assessments

Sang Mok Park, Michelle Visbal-Onufrak, Md Munirul Haque, Martin Were, Violet Naanyu, Md Kamrul Hasan, Young Kim

Proceedings Volume 11632, 116320L (2021) https://doi.org/10.1117/12.2578640

KEYWORDS: Blood, Spectroscopy, Medicine, Transform theory, Tissue optics, Imaging systems, Imaging spectroscopy, Image analysis, Hyperspectral imaging, Cameras

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In developing countries, anemia is a major public health problem; anemia affects 24.8% of the global population, corresponding to 1.62 billion people. As anemia is defined as a low hemoglobin level in the blood, it is important to measure exact hemoglobin content in grams per deciliter of the blood. Recent advances in mobile health (mHealth) technologies for blood hemoglobin levels are promising, but often rely on additional complex components to the smartphone and require blood sampling. As a result, noninvasive quantification of hemoglobin content in the blood is still limited. We have recently developed intravital mHealth spectroscopy to extract spectrally encoded microvascular and blood information from peripheral tissue. Spectral learning virtually transforms the built-in camera of a smartphone into a hyperspectral imager for spectroscopic analyses. Owing to the easy accessibility and relatively uniform microvasculature, the inner eyelid is used as a sensing site. Spectroscopic analyses of spectra acquired from the inner eyelid further result in key parameters about the blood and the microvasculature that are used for predicting blood hemoglobin levels in a noninvasive and real-time manner. Our clinical study conducted in sub-Saharan Africa supports reliable performance of blood hemoglobin quantification and anemia prediction. As our mHealth technology requires no additional attachment (our data-centric approach minimizes hardware complexity), the key features include mobility, simplicity, and affordability for rapid and scalable adaptation. Successful implementation with local governments and community healthcare workers can potentially provide unreached and underserved remote populations with accessible and affordable healthcare services in low-resource settings.

Proceedings Article | 5 March 2021 Presentation + Paper

Spectral super-resolution spectroscopy for biomedical applications

Sang Mok Park, Young Kim

Proceedings Volume 11656, 116560N (2021) https://doi.org/10.1117/12.2577799

KEYWORDS: Super resolution, Spectroscopy, Biomedical optics, Hyperspectral imaging, Tissue optics, Sensors, RGB color model, Imaging spectroscopy, Cameras, Spectral resolution

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Reflectance spectroscopy and hyperspectral (or multispectral) imaging that can acquire a matrix of intensity as a function of the position and the wavelength of light (also known as a hypercube) are extensively used to quantify biochemical composition, structure, and vasculature in biological tissue. However, these methods often rely on bulky and costly optical components, which limit the development of compact, rapid, and cost-effective technologies. Fortuitously, several different research communities have demonstrated that it is possible to mathematically reconstruct hyperspectral (with high spectral resolution) or multispectral data from RGB images taken by a conventional camera (three-color sensor). However, these methods, such as compressive (compressed) sensing and deep learning, are often limited for extracting detailed biological spectral profiles and require an extremely large amount of training data. We have recently developed a spectral super-resolution framework that enables us to virtually transform the built-in camera (RGB sensor) of a smartphone into a hyperspectral imager for accurate and precise spectroscopic analyses, without a need for any hardware modifications or accessories. Super-resolution means high-resolution reconstruction of digital images acquired with lowresolution systems. We have extended this concept to the frequency domain for hyperspectral imaging, which has numerous biomedical applications. As an example, our mobile version of spectral super-resolution combines imaging of peripheral tissue and spectroscopic quantification of blood hemoglobin levels in a noninvasive manner. Spectral superresolution spectroscopy can also serve as an example that data-driven technologies can minimize hardware complexity, facilitating the tempo of clinical translation.

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