Non-invasive optical blood flow monitoring systems for disease diagnosis and healthcare monitoring have been studied. Diffuse Speckle Contrast Analysis (DSCA) system can measure deep-tissue blood flow with a relatively simple system configuration, high speed, and high sensitivity. However, the relative blood flow index (BFI) is acquired with the system, and it changes with every acquisition. In this study, we adopt machine learning to overcome this limitation. DSCA system was established with a micro-size camera, and the correlation between conventional BFI and ML-based BFI was analyzed. This work will be the first step toward a quantitative Diffuse Speckle Contrast Velocimetry (DSCV).
Many studies on diagnosing adult chronic diseases such as diabetes have been achieved by analyzing blood data. Here, we present machine learning algorithm-based diagnostic methods for diabetes by analyzing blood flow oscillations. We used diffuse speckle contrast analysis(DSCA) to measure the blood flow of rats. It can non-invasively measure changes in the relative blood flow of the tissue. Blood flow data acquired from Streptozotocin-induced and control rats were preprocessed by wavelet transform and then classified from machine learning algorithms. In conclusion, the machine learning method can successfully classify two blood flow oscillations in diabetic and control rats.
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