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In this fingernail-based mobile app, the blood Hgb prediction algorithm is built using skin color data of images and image metadata. Smartphone RGB images of fingernails also enable noninvasive blood Hgb measurements and anemia assessments 2.
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This mobile app analyzes blood pulse signals from a series of RGB images of the fingertip with feature extraction methods. HemaApp uses the built-in camera of a smartphone to estimate blood Hgb levels 1. It should be noted that when the accuracy and precision of blood Hgb measurements are limited, anemia screening, which categorize low, moderate, or normal blood Hgb levels, is often considered as the primary use case. Even though these devices allow real-time and continuous monitoring of blood Hgb levels based on advanced processing of optical signals, specialized equipment with a relatively high cost is still required in a hospital setting.Īs the ownership of smartphones dramatically increases and mobile health (mHealth) technologies have received considerable attention, noninvasive blood Hgb measurements and anemia assessments using mobile devices or smartphones have made progresses.
In particular, noninvasive devices for measuring blood Hgb content are commercially available, such as Masimo and OrSense. To overcome this limitation, several different technologies are currently being developed for noninvasively measuring blood hemoglobin (Hgb) levels and anemia assessments. CURRENT NONINVASIVE TECHNOLOGIES FOR ANEMIA ASSESSMENTS 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.ġ. 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. Our clinical study conducted in sub-Saharan Africa supports reliable performance of blood hemoglobin quantification and anemia prediction. 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. Owing to the easy accessibility and relatively uniform microvasculature, the inner eyelid is used as a sensing site. Spectral learning virtually transforms the built-in camera of a smartphone into a hyperspectral imager for spectroscopic analyses. We have recently developed intravital mHealth spectroscopy to extract spectrally encoded microvascular and blood information from peripheral tissue. As a result, noninvasive quantification of hemoglobin content in the blood is still limited. 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 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. In developing countries, anemia is a major public health problem anemia affects 24.8% of the global population, corresponding to 1.62 billion people.