The huge surge in smartphone users in the last decade has provided an unprecedented opportunity for App-based diagnosis of infectious and chronic diseases. The internet access has further enhanced the prospects of smartphone-based diagnosis as the data captured can be stored in cloud, analyzed remotely and, the finding may then be placed onto the cloud or communicated through the phone. In this work, commercially available urine strips (Uristix Siemens), chemically impregnated with Bromophenol blue, were used to quantify the concentrations of albumin using a smartphone device i.e. iPhone SE. A Tungsten lamp of color temperature 3500 K was used to illuminate the urine strips. The images of the dipstick after being dipped into different concentrations were captured using a smartphone. The RGB color values were converted into tristimulus values to calibrate the chromaticity curve. Further, the concentration of test samples was calculated using a calibration curve based on a nearest neighbour algorithm. Calibration was done using six different albumin concentrations: 160 mg/L, 320 mg/L, 640 mg/L, 2560 mg/L, 5120 mg/L, and 10240 mg/L. We were able to estimate the albumin concentration in the range of 160 - 10240 mg/L using the proposed algorithm. Three different illuminations i. e. 500 Lux, 400 Lux, and 300 Lux were used to check the robustness of the algorithm. The correlation coefficient for the estimation of albumin concentration was found to be ~ 0.94. This may help to monitor the progression of kidney disease and cardiovascular diseases.
Detection of proteinuria in urine is an early sign of the progression of cardiovascular and chronic kidney disease. In general, colorimetry detection in urine dipsticks is carried out for a screening of proteinuria. Although low sensitivity and vision assessment has hindered its utility. Smartphone-based colorimetry detection using a urine dipstick plays an important role to estimate the concentration of albumin. Segmentation of albumin patches in urine dipstick is the first step for the automatic estimation of albumin concentration in the urine sample. Here, we present a deep learning-based object detection technique i.e. You Only Look Once (YOLOv5), for the segmentation of albumin patches from urine dipstick (Uristix Siemens) for the estimation of albumin concentration. A comparison between different segmentation techniques to accurately arrive at the region of interest has been demonstrated. Four different smartphones i.e. iPhone SE, Realme C11, Redmi 8A Dual, and Samsung Galaxy M01 were used to capture the images of the urine dipstick, at six different illumination conditions i. e. 500 Lux, 400 Lux, 300 Lux, 200 Lux, 100 Lux, and 50 Lux. The training was done using iPhone SE with eight different albumin concentrations: 10 mg/L, 20 mg/L, 40 mg/L, 80 mg/L, 160 mg/L, 320 mg/L, 640 mg/L, and 1280 mg/L. The proposed model is fast and robust and it helps minimize the effect of different light conditions and color variations across smartphone models.
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