Title: Rapid point-of-care Hemoglobin measurement through low-cost optics and Convolutional Neural Network based validation,
Abstract: A low-cost, robust, and simple mechanism to measure hemoglobin would play a
critical role in the modern health infrastructure. Consistent sample
acquisition has been a long-standing technical hurdle for photometer-based
portable hemoglobin detectors which rely on micro cuvettes and dry chemistry.
Any particulates (e.g. intact red blood cells (RBCs), microbubbles, etc.) in a
cuvette's sensing area drastically impact optical absorption profile, and
commercial hemoglobinometers lack the ability to automatically detect faulty
samples. We present the ground-up development of a portable, low-cost and open
platform with equivalent accuracy to medical-grade devices, with the addition
of CNN-based image processing for rapid sample viability prechecks. The
developed platform has demonstrated precision to the nearest $0.18[g/dL]$ of
hemoglobin, an R^2 = 0.945 correlation to hemoglobin absorption curves reported
in literature, and a 97% detection accuracy of poorly-prepared samples. We see
the developed hemoglobin device/ML platform having massive implications in
rural medicine, and consider it an excellent springboard for robust deep
learning optical spectroscopy: a currently untapped source of data for
detection of countless analytes.