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A systematic approach for the accurate non-invasive estimation of blood glucose utilizing a novel light-tissue interaction adaptive modelling scheme
Diabetes is one of the biggest health challenges of the 21st century. The obesity epidemic, sedentary lifestyles and an ageing population mean prevalence of the condition is currently doubling every generation. Diabetes is associated with serious chronic ill health, disability and premature mortality. Long-term complications including heart disease, stroke, blindness, kidney disease and amputations, make the greatest contribution to the costs of diabetes care. Many of these long-term effects could be avoided with earlier, more effective monitoring and treatment. Currently, blood glucose can only be monitored through the use of invasive techniques. To date there is no widely accepted and readily available non-invasive monitoring technique to measure blood glucose despite the many attempts. This paper challenges one of the most difficult non-invasive monitoring techniques, that of blood glucose, and proposes a new novel approach that will enable the accurate, and calibration free estimation of glucose concentration in blood. This approach is based on spectroscopic techniques and a new adaptive modelling scheme. The theoretical implementation and the effectiveness of the adaptive modelling scheme for this application has been described and a detailed mathematical evaluation has been employed to prove that such a scheme has the capability of extracting accurately the concentration of glucose from a complex biological media
Evaluation of a fiberoptic-based system for measurement of optical properties in highly attenuating turbid media
BACKGROUND: Accurate measurements of the optical properties of biological tissue in the ultraviolet A and short visible wavelengths are needed to achieve a quantitative understanding of novel optical diagnostic devices. Currently, there is minimal information on optical property measurement approaches that are appropriate for in vivo measurements in highly absorbing and scattering tissues. We describe a novel fiberoptic-based reflectance system for measurement of optical properties in highly attenuating turbid media and provide an extensive in vitro evaluation of its accuracy. The influence of collecting reflectance at the illumination fiber on estimation accuracy is also investigated. METHODS: A neural network algorithm and reflectance distributions from Monte Carlo simulations were used to generate predictive models based on the two geometries. Absolute measurements of diffuse reflectance were enabled through calibration of the reflectance system. Spatially-resolved reflectance distributions were measured in tissue phantoms at 405 nm for absorption coefficients (ÎĽ(a)) from 1 to 25 cm(-1 )and reduced scattering coefficients ([Formula: see text]) from 5 to 25 cm(-1). These data and predictive models were used to estimate the optical properties of tissue-simulating phantoms. RESULTS: By comparing predicted and known optical properties, the average errors for ÎĽ(a )and [Formula: see text] were found to be 3.0% and 4.6%, respectively, for a linear probe approach. When bifurcated probe data was included and samples with ÎĽ(a )values less than 5 cm(-1 )were excluded, predictive errors for ÎĽ(a )and [Formula: see text] were further reduced to 1.8% and 3.5%. CONCLUSION: Improvements in system design have led to significant reductions in optical property estimation error. While the incorporation of a bifurcated illumination fiber shows promise for improving the accuracy of [Formula: see text] estimates, further study of this approach is needed to elucidate the source of discrepancies between measurements and simulation results at low ÎĽ(a )values