Smartphone-based, automated detection of urine albumin using deep learning approach

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

doi:10.1016/j.measurement.2022.110948

Cited by 16 publications

(13 citation statements)

References 22 publications

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Section: Related Workmentioning

confidence: 99%

“…Among ML techniques DNNs excel in image processing [23,50,51]. In [23] a CNN classified albumin concentration values in urine from a paper-based assay using different smartphones' cameras, evaluating also the performance improvement gained using the smartphones' flash. This method however does not provide any additional information to the neural network about the lighting in which the test is taken, with an impact on the overall accuracy.…”

Section: Related Workmentioning

confidence: 99%

“…Experimental results on real data confirmed the reliability of the proposed approach. The custom CNN architecture yielded from the NAS outperformed state-of-the-art solutions targeting similar problems [22,23], showing up to a 14.8% increment in accuracy on a 10-class classification problem.…”

Section: Introductionmentioning

confidence: 99%

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Semiquantitative Determination of Thiocyanate in Saliva Through Colorimetric Assays: Design of CNN Architecture via Input-Aware NAS

Taccioli,

Ragusa,

Pomili

et al. 2023

IEEE Sensors J.

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This paper presents a novel method enabling point-ofcare testing of thiocyanate concentration in saliva. Thiocyanate is an important biological marker; its levels are linked with diseases such as cancer and neurodegeneration. Hence, monitoring this marker frequently can positively impact users' lives. In the proposed setup, the goal is a semi-quantitative reading of thiocyanate concentration from colorimetric assays in solution; the user-friendly yet accurate readout procedure relies on a smartphone camera and is designed to be robust against moderate changes in indoor lighting conditions. The readout procedure exploits the capabilities of Convolutional Neural Networks (CNNs) to fully profit from a setup involving a custom color chart and the assay vial. Thus, a data-driven strategy is adopted to deal with color distortions caused both by lighting conditions and by post-processing operations embedded in the smartphone camera. A Neural Architecture Search (NAS) procedure explicitly tuned for the problem at hand drove the design of the custom CNN architecture. The method has been tested using a collection of real-world data and compared with existing approaches. The results presented in this paper show an increase in accuracy up to about 14% with respect to state-of-the-art methods.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

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Semiquantitative Determination of Thiocyanate in Saliva Through Colorimetric Assays: Design of CNN Architecture via Input-Aware NAS

Taccioli,

Ragusa,

Pomili

et al. 2023

IEEE Sensors J.

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“…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]. In 2022, Kim and Cho used an iPhone 11 as a urine analyzer to detect albumin using a machine-learning tool [30].…”

Section: Introductionmentioning

confidence: 99%

“…• 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%

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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Machine learning-assisted image label-free smartphone platform for rapid segmentation and robust multi-urinalysis

Xu,

Yan,

Gui

et al. 2024

Anal Bioanal Chem

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Smartphone-based, automated detection of urine albumin using deep learning approach

Cited by 16 publications

References 22 publications

Semiquantitative Determination of Thiocyanate in Saliva Through Colorimetric Assays: Design of CNN Architecture via Input-Aware NAS

Semiquantitative Determination of Thiocyanate in Saliva Through Colorimetric Assays: Design of CNN Architecture via Input-Aware NAS

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

Machine learning-assisted image label-free smartphone platform for rapid segmentation and robust multi-urinalysis

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