Machine Learning-Based Rapid Diagnostic-Test Reader for Albuminuria Using Smartphone

Thakur, Ritambhara; Maheshwari, Prateek; Datta, Sudip Kumar; Dubey, Swapnil; Shakher, Chandra

doi:10.1109/jsen.2020.3034904

Cited by 22 publications

(9 citation statements)

References 33 publications

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“…Trace albuminuria is a high-risk condition affects 6% to 9% of the adult population and major cause of shortening one's life span by up to 7 years 1 . Urine dipstick method is the conventional method for screening of albuminuria 2 . Generally, colorimetry detection is carried out by visual inspection which is more prone to error.…”

Section: Introductionmentioning

confidence: 99%

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Deep-learning-based color image segmentation for biological application

Bhatt

Goel

Datta

et al. 2023

Women in Optics and Photonics in India 2022

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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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Section: Introductionmentioning

confidence: 99%

“…To check the robustness of the proposed model, four different smartphones were used to acquire the images at different light conditions. Further, the segmented image could be used for the estimation of albumin concentration in urine samples 2 .…”

Section: Introductionmentioning

confidence: 99%

Deep-learning-based color image segmentation for biological application

Bhatt

Goel

Datta

et al. 2023

Women in Optics and Photonics in India 2022

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“…• Most of them have used only a standard albumin solution [33,34], which is pure in nature, whereas patient or clinical urine is a matrix of analytes. Detection of a specific analyte (e.g.…”

Section: Introductionmentioning

confidence: 99%

“…Solmaz et al used the SVM algorithm using RGB, Lab, and HSV to estimate hydrogen peroxide [32]. In 2020, Thakur et al estimated albumin concentration using three different smartphone models [33] and suggested that the shadow effect and ambient light problem can be resolved using machine-learning tools, such as RF. Furthermore, Thakur et al reported a deeplearning-based approach to estimate the albumin concentration in standard solutions of albumin and resolved the issues associated with ambient light and interphone repeatability using a customized CNN [34].…”

Section: Introductionmentioning

confidence: 99%

Colorimetry-based and smartphone-assisted machine-learning model for quantification of urinary albumin

Bhatt,

Kumar,

Gupta

et al. 2023

Meas. Sci. Technol.

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Presence of albumin in urine is indicative of kidney damage and can happen due to several underlying conditions like diabetes. The concentration of albumin in urine is used for diagnosis and staging of chronic kidney disease. In clinical samples, detection of albumin at lower concentrations is very crucial for early diagnosis and monitoring of chronic kidney disease (CKD). Current urine analyzers precisely quantify albumin but are expensive and difficult to use in point-of-care (PoC) settings. Here, we demonstrate the quantification of albumin concentration in urine sample using colorimetry. This model presents an accessory-free urine analyzer that uses a smartphone and customized machine learning algorithms. Here, urine sample is introduced onto the chemically impregnated dipstick that exhibits the change in its color with the amount of albumin. The images of the urine dipsticks are captured using the camera of the smartphone under different illumination/experimental conditions and are processed to extract the change in the color values arising due to change in the concentration of the urinary albumin. Concentrations of the albumin are estimated from the change in color values. We have used customized machine learning algorithms for the classification of albumin concentrations and to mitigate the effect of ambient light conditions. The k-Nearest Neighbor (kNN) algorithm yielded an average classification accuracy of 96% with detection limit of 4 mg/L. Proposed scheme can be extensively used to monitor albumin concentration in PoC settings.

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“…In addition, several smartphone-based analyzers have been developed for quantitative analysis of dry-reagent dipsticks. 4,5,[10][11][12] These devices offer unique advantages in proactive home healthcare due to their easy access to the Internet of Medical Things (IoMT) via a smartphone. IoMT is a platform to interconnect and integrate medical-grade devices with health networks.…”

Section: Introductionmentioning

confidence: 99%