Color Space Transformation-Based Smartphone Algorithm for Colorimetric Urinalysis

Point-of-care image-based quantitative urinalysis with commercial reagent strips: Design and clinical evaluation

Urinalysis is a useful test as an indicator of health or disease and as such, is a part of routine health screening. Urinalysis can be undertaken in many ways, one of which is reagent strips used in the general evaluation of health and to aid in the diagnosis and monitoring of kidney disease. To be effective, the test must be performed properly, and the results interpreted correctly. However, different light conditions and colour perception can vary between users leading to ambiguous readings. This has led to camera devices being used to capture and generate the estimated biomarker concentrations, but image colour can be affected by variations in illumination and inbuilt image processing. Therefore, a new portable device with embedded image processing techniques is presented in this study to provide quantitative measurements that are invariant to changes in illumination. The device includes a novel calibration process and uses the ratio of RGB values to compensate for variations in illumination across an image and improve the accuracy of quantitative measurements. Results show that the proposed calibration method gives consistent homogeneous illumination across the whole image. Comparisons against other existing methods and clinical results show good performance with a correlation to the clinical values. The proposed device can be used for point-of-care testing to provide reliable results consistent with clinical values.

Paper-based uric acid assay in whole blood samples by Zn2+ protein precipitation and enzyme-free colorimetric detection

Uric acid (UA) is an important biomarker, as a high concentration in blood can lead to gout and further renal syndrome. Although several point-of-care testing (POCT) devices have been reported to detect UA, there are some limitations such as the requirement for uricase and the complicated pretreatment of serum/plasma samples, which restricts their use at home or in undeveloped areas. In this work, we developed an approach by applying Zn2+ to precipitate proteins and cells in whole blood to avoid interference with the chromogenic reaction. We used carboxymethylcellulose (CMC) to immobilize tetramethylbenzidine (TMB) on a nitrocellulose membrane for colorimetric detection. Using the oxidization properties of H2O2, which turns TMB into oxidized tetramethylbenzidine (TMBox) in the presence of catalyst gold nanoparticles (AuNPs), we successfully constructed an enzyme-free paper-based POCT device using the reduction reaction of UA and TMBox for simple, speedy, and cheap colorimetric detection of UA, achieving a detection time of 8 min, a linear range of 0-150 μg/mL, and an LOD of 25.79 μg/mL. The UA concentration in whole blood samples was further measured and correlated well with the clinical value (R2 = 0.8212). Thus, the proposed assay has the potential for POCT diagnosis, monitoring, and prognosis of diseases related to UA.

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

This study presents a groundbreaking approach for the early detection of chronic kidney disease (CKD) and other urological disorders through an image-label-free, multi-dipstick identification method, eliminating the need for complex machinery, label libraries, or preset coordinates. Our research successfully identified reaction pads on 187 multi-dipsticks, each with 11 pads, leveraging machine learning algorithms trained on human urine data. This technique aims to surpass traditional colourimetric methods and concentration-colour curve fitting, offering more robust and precise community screening and home monitoring capabilities. The developed algorithms enhance the generalizability of machine learning models by extracting primary colours and correcting urine colours on each reaction pad. This method's cost-effectiveness and portability are significant, as it requires no additional equipment beyond a standard smartphone. The system's performance rivals professional medical equipment without auxiliary lighting or flash under regular indoor light conditions, effectively managing false positives and negatives across various categories with remarkable accuracy. In a controlled experimental setting, we found that random forest algorithms, based on a Bagging strategy and applied in the HSV colour space, showed optimal results in smartphone-assisted urinalysis. This study also introduces a novel urine colour correction method, significantly improving machine learning model performance. Additionally, ISO parameters were identified as crucial factors influencing the accuracy of smartphone-based urinalysis in the absence of additional lighting or optical configurations, highlighting the potential of this technology in low-resource settings.

A dual-ligand lanthanide-based metal-organic framework for highly selective and sensitive colorimetric detection of Fe2

Accumulation of heavy metals in humans and mammals causes health problems due to their abundance as transition metal ions. Iron (Fe2+) serves significantly in numerous biological processes as a heavy metal ion. In this study, we have designed and prepared a metal-organic framework (MOF) utilizing a one-step solvothermal process, incorporating a dual-ligand combination of terephthalic acid (H2BDC) and α,α',α''-tert-pyridine (TPY) with Eu3+ as the metal node. For this MOF, we termed it Eu-BDC/TPY. Eu-BDC/TPY has superior selectivity over other metal cations. It provides an accurate, sensitive, broad linear range colorimetric method for detecting Fe2+ in a concentration range of 1-50 μM with a modest limit of detection (0.33 μM). Eu-BDC/TPY detects the absence of Fe2+ quickly (within 5 seconds), which is very valuable in practical applications. In addition, the results can be used to create a digital image colorimetric card (DIC) using colorimetric software, enabling instantaneous detection of Fe2+ concentration using a smartphone.

Current trends in digital camera-based bioassays for point-of-care tests

Point-of-care and bedside tests are analytical devices suitable for a growing role in the current healthcare system and provide the opportunity to achieve an exact diagnosis by an untrained person and in various conditions and sites where it is necessary. Using a digital camera integrated into a well-accessible device like a smartphone brings a new way in which a colorimetric point-of-care diagnostic test can provide unbiased data. This review summarizes basic facts about the colorimetric point-of-care tests, principles of how to use a portable device with a camera in the assay, applications of digital cameras for the current tests, and new devices described in the recent papers. An overview of the recent literature and a discussion of recent developments and future trends are provided.

Evaluation of a smartphone-based colorimetric method for urinalysis dipstick readings in cats

OBJECTIVES

The aim of the study was to compare the diagnostic performances of a smartphone-based colorimetric method (SBCM) for urinalysis with a semi-automated point-of-care (POC) analyser using standardised solutions and cat urine.

METHODS

Artificial solutions (negative and positive quality controls, and purposely designed artificial urine) and natural urine from 216 cats were used. Two urine reagent strips were simultaneously dipped in each sample. One dipstick was read by the SBCM and the other by the POC analyser at the same time. Results for pH, proteins, bilirubin, 'blood', glucose and ketones were considered. Overall agreement and sensitivity, specificity and accuracy of the SBCM were determined based on selected cut-offs.

RESULTS

For the artificial solutions, 80 comparisons were obtained for each analyte and each expected concentration. The overall agreement (exactly the same result) between the two methods was 78.4%. SBCM sensitivity, specificity and accuracy were 99.0%, 100% and 99.3%, respectively. The correlation between the two methods was almost perfect (Cohen's kappa coefficient = 0.9851). For natural urine samples, the overall agreement (including pH) was 68.6%. Using optimal cut-offs for the SBCM determined from the results of analysis of artificial solutions, the sensitivity, specificity and accuracy of the SBCM were 100%, 76.02% and 80.5%, respectively. In this situation, the correlation between the two methods was moderate (Cohen's kappa coefficient = 0.5401). This was mostly due to a high rate of false-positive results for bilirubin (61.1%).

CONCLUSIONS AND RELEVANCE

With proper cut-off use (ie, considering positive or negative results) the SBCM evaluated here has a perfect sensitivity and appropriate diagnostic performances for proteins, 'blood', glucose and ketones. Based on these experimental data, this method appears suitable for dipstick urinalysis but positive results for bilirubin and proteins have to be confirmed.

Leukocyte Esterase Strip Quantitative Detection Based on RGB Photometry is a Probable Method to Diagnose Periprosthetic Joint Infection: An Exploratory Study

OBJECTIVE

Leucocyte esterase (LE) strip test is the most rapid, convenient, and cheap method to diagnose chronic periprosthesis joint infection (PJI). However, the determination of LE strip mainly relies on colorimetric method with strong subjectivity, which leads to low diagnostic accuracy. Therefore, we try to convert LE strip images into digital data through the RGB photometric system to achieve objective diagnosis. This method will greatly improve the accuracy of LE strip detection and diagnosis of PJI.

METHODS

From January 2021 to September 2021, 46 patients with suspected PJI after total hip and knee arthroplasty underwent diagnostic joint puncture. After effective joint fluid samples were harvested, they were divided into original fluid and centrifuged fluid for LE strip detection. Real-time images of LE strip were taken at 90 s, 3 min, 5 min, 10 min, and 15 min after sampling, and their brightness (Y) was obtained after they were input into an RGB photometric system. Grouping was based on centrifugation, infection, and time points, and then the differences in brightness among groups were compared. The correlation between LE strip image brightness and WBC count was evaluated. Student t-test was used for the parametric data and chi-square test for qualitative data. Simple linear regression was utilized to analyze the correlation between brightness and WBC count in each group.

RESULTS

Included were 19 cases of PJI and 27 Non-PJI subjects diagnosed against ICM2018 diagnostic criteria. The brightness was lower in the PJI group than in Non-PJI group (p < 0.05). The brightness of the uncentrifuged group was lower than that of the centrifuged group (p < 0.05). Irrespective of centrifugation or infection, the brightness of LE strip decreased with the exposure time after sampling. The brightness of LE strip was correlated with WBC count at different time points, with the correlation being strongest 5 min after sampling (R² (5 min) = 0.86, p < 0.0001). The correlation between LE strip brightness and WBC count was also found in the centrifugation group, with the correlation being most robust 15 min after sampling (R² (15 min) = 0.73, p < 0.0001).

CONCLUSION

A remarkable correlation was found between LE strip brightness and the WBC count. It is feasible to directly quantify LE strip image on a RGB photometer to achieve quantitative detection of LE strip to diagnose PJI.

Performance evaluation of Hipee S2 point-of-care testing urine dipstick analyser: a cross-sectional study

OBJECTIVE

With advances in mobile technology, smartphone-based point-of-care testing (POCT) urinalysis hold great potential for disease screening and health management for clinicians and individual users. The purpose of this study is to evaluate the analytical performance of Hipee S2 POCT urine dipstick analyser.

DESIGN

A multicentre, hospital-based, cross-sectional study.

SETTING

Analytical performance of the POCT analyser was conducted at a clinical laboratory, and method comparison was performed at three clinical laboratories in China.

PARTICIPANTS

Urine samples were collected from 1603 outpatients and inpatients at three hospitals, and 5 health check-up population at one of the hospitals.

OUTCOME MEASURES

All tests were performed by clinical laboratory technicians. Precision, drift, carry-over, interference and method comparison of Hipee S2 were evaluated. Diagnostic accuracy of semiquantitative albumin-to-creatinine ratio (ACR) for albuminuria was carried out using quantitative ACR as the standard.

RESULTS

The precision for each parameter, assessed by control materials, was acceptable. No sample carry-over or drift was observed. Ascorbate solution with 1 g/L had an inhibitory effect for the haemoglobin test. Agreement for specific gravity (SG) varied between moderate to substantial (κ values 0.496-0.687), for pH was moderate (κ values 0.423-0.569) and for other parameters varied between substantial to excellent (κ values 0.669-0.991), on comparing the Hipee S2 with laboratory analysers. The semiquantitative microalbumin and creatinine were highly correlated with the quantitative results. The sensitivity of semiquantitative ACR to detect albuminuria was 87.2%-90.7%, specificity was 70.7%-78.4%, negative predictive value was 85.3%-87.9% and positive predictive value was 73.9%-83%.

CONCLUSIONS

Hipee S2 POCT urine analyser showed acceptable analytical performance as a semiquantitative method. It serves as a convenient alternate device for clinicians and individual users for urinalysis and health management. In addition, the POCT semiquantitative ACR would be useful in screening for albuminuria.

Application of Janus Particles in Point-of-Care Testing

Janus particles (JPs), named after the two-faced Roman god, are asymmetric particles with different chemical properties or polarities. JPs have been widely used in the biomedical field in recent years, including as drug carriers for targeted controlled drug release and as biosensors for biological imaging and biomarker detection, which is crucial in the early detection and treatment of diseases. In this review, we highlight the most recent advancements made with regard to Janus particles in point-of-care testing (POCT). Firstly, we introduce several commonly used methods for preparing Janus particles. Secondly, we present biomarker detection using JPs based on various detection methods to achieve the goal of POCT. Finally, we discuss the challenges and opportunities for developing Janus particles in POCT. This review will facilitate the development of POCT biosensing devices based on the unique properties of Janus particles.

Predictive System Implementation to Improve the Accuracy of Urine Self-Diagnosis with Smartphones: Application of a Confusion Matrix-Based Learning Model through RGB Semiquantitative Analysis

Urinalysis, an elementary chemical reaction-based method for analyzing color conversion factors, facilitates examination of pathological conditions in the human body. Recently, considerable urinalysis-centered research has been conducted on the analysis of urine dipstick colors using smartphone cameras; however, such methods have a drawback: the problem of reproducibility of accuracy through quantitative analysis. In this study, to solve this problem, the function values for each concentration of a range of analysis factors were implemented in an algorithm through urine dipstick RGB semi-quantitative color analysis to enable real-time results. Herein, pH, glucose, ketones, hemoglobin, bilirubin, protein (albumin), and nitrites were selected as analysis factors, and the accuracy levels of the existing equipment and the test application were compared and evaluated using artificial urine. In the semi-quantitative analysis, the red (R), green (G), and blue (B) characteristic values were analyzed by extracting the RGB characteristic values of the analysis factors for each concentration of artificial urine and obtaining linear function values. In addition, to improve the reproducibility of detection accuracy, the measurement value of the existing test equipment was set to an absolute value; using a machine-learning technique, the confusion matrix, we attempted to stabilize test results that vary with environment.

A Simple and Reliable Dispersive Liquid-Liquid Microextraction with Smartphone-Based Digital Images for Determination of Carbaryl Residues in Andrographis paniculata Herbal Medicines Using Simple Peroxidase Extract from Senna siamea Lam. Bark

A simple and reliable dispersive liquid-liquid microextraction (DLLME) coupled with smartphone-based digital images using crude peroxidase extracts from cassia bark (Senna siamea Lam.) was proposed to determine carbaryl residues in Andrographis paniculata herbal medicines. The method was based on the reaction of 1-naphthol (hydrolysis of carbaryl) with 4-aminoantipyrine (4-AP) in the presence of hydrogen peroxide, using peroxidase enzyme simple extracts from cassia bark as biocatalysts under pH 6.0. The red product, after preconcentration by DLLME using dichloromethane as extraction solvent, was measured for blue intensity by daily life smartphone-based digital image analysis. Under optimized conditions, good linearity of the calibration graph was found at 0.10–0.50 mg·L⁻¹ (r² = 0.9932). Limits of detection (LOD) (3SD/slope) and quantification (LOQ) (10SD/slope) were 0.03 and 0.09 mg·L⁻¹, respectively, with a precision of less than 5%. Accuracy of the proposed method as percentage recovery gave satisfactory results. The proposed method was successfully applied to analyze carbaryl in Andrographis paniculata herbal medicines. Results agreed well with values obtained from the HPLC-UV method at 95% confidence level. This was simple, convenient, reliable, cost-effective and traceable as an alternative method for the determination of carbaryl.

Development of a smartphone-based lateral-flow imaging system using machine-learning classifiers for detection of Salmonella spp

Salmonella spp. are a foodborne pathogen frequently found in raw meat, egg products, and milk. Salmonella is responsible for numerous outbreaks, becoming a frequent major public-health concern. Many studies have recently reported handheld and rapid devices for microbial detection. This study explored a smartphone-based lateral-flow assay analyzer which employed machine-learning algorithms to detect various concentrations of Salmonella spp. from the test line images. When cell numbers are low, a faint test line is difficult to detect, leading to misleading results. Hence, this study focused on the development of a smartphone-based lateral-flow assay (SLFA) to distinguish ambiguous concentrations of test line with higher confidence. A smartphone cradle was designed with an angled slot to maximize the intensity, and the optimal direction of the optimal incident light was found. Furthermore, the combination of color spaces and the machine-learning algorithms were applied to the SLFA for classifications. It was found that the combination of L*a*b and RGB color space with SVM and KNN classifiers achieved the high accuracy (95.56%). A blind test was conducted to evaluate the performance of devices; the results by machine-learning techniques reported less error than visual inspection. The smartphone-based lateral-flow assay provided accurate interpretation with a detection limit of 5 × 10⁴ CFU/mL commercially available lateral-flow assays.

Development of smart core-shell nanoparticle-based sensors for the point-of-care detection of alpha amylase in diagnostics and forensics

Smart biocompatible materials, responsive to various external stimuli, hold immense potential in the development of biosensors for low-cost diagnostics. The present paper outlines the development of smart enzyme-responsive core-shell nanoparticle-based sensors as low-cost diagnostics for alpha amylase detection. The biocompatible core-shell nanoparticles of 200-250 nm size consisted of a chitosan-tripolyphosphate core formed by ionic gelation coated with a starch-iodine shell. In the presence of specific concentrations of amylase, the starch-iodine shell was disrupted and resulted in the exposure of core. This application herein describes a visible switch in color from blue to red towards the point-of-care detection of salivary alpha amylase (sAA). Stress and other autonomic disturbances can be diagnosed by measuring this biomarker. Also, alpha amylase can be used in the detection of latent saliva at crime scenes for forensic investigations. Using the present platform technology, a paper-based diagnostic was developed for detection of salivary alpha amylase that demonstrated a limit of detection (LoD) of 140 units/ml (70 mg/ml) at 5 minutes while a coated swab developed from the nanoparticles for crime scene investigations could achieve an LoD of 2.5 units/ml (1.25 mg/ml) over 30 minutes. The nanoparticles demonstrated stability and reproducibility with no interference seen with other substances in saliva. The present paper provides a proof-of-concept technology underscoring the utility of smart nanoparticles in affordable, versatile biosensing platforms like paper-based and swab-based formats for such diverse applications as diagnostics for stress and in forensics.

Gold nanoparticle based colorimetric assay of telomerase activity using the cyclic strand displacement reaction

A facile colorimetric assay is developed for the detection of telomerase activity based on the cyclic strand displacement reaction.

High-Performance Estimation of Lead Ion Concentration Using Smartphone-Based Colorimetric Analysis and a Machine Learning Approach

Traditional methods for detection of lead ions in water samples are costly and time-consuming. In this work, an accurate smartphone-based colorimetric sensor was developed utilizing a novel machine learning algorithm. In the presence of Pb²⁺ ions in the solution of specifically functionalized gold nanoparticles, the color of solution turns from red to purple. Indeed, the color variation of the solution is proportional to Pb²⁺ concentration. The smartphone camera captures the corresponding color change, and the image is processed by an efficient artificial intelligence protocol. The nonlinear regression approach was used for concentration estimation, in which the parameters of the proposed model are obtained using a new feature extraction algorithm. In prediction of Pb²⁺ concentration, the average absolute error and root-mean-square error were 0.094 and 0.124, respectively. The influence of pH of the medium, temperature, oligonucleotide concentration, and reaction time on the performance of the proposed sensor was carefully investigated and understood to achieve the best sensor response. This novel sensor exhibited good linearity for the detection of Pb²⁺ in the concentration range of 0.5-2000 ppb with a detection limit of 0.5 ppb.

Digital image colorimetry detection of carbaryl in food samples based on liquid phase microextraction coupled with a microfluidic thread-based analytical device

This research used a digital image colorimetry (DIC) method to detect carbaryl in food samples using effervescence-assisted liquid phase microextraction based on solidification of switchable hydrophilicity solvent combined with a microfluidic thread-based analytical device (EA-LPME-SSHS-μTAD). 1-naphthol, the hydrolysate of carbaryl, was extracted into octanoic acid by the adjustment of pH values of the sample solution and separated through solidification in an ice bath. Then 1-naphthol contained in the extracted solution was coupled with 4-methoxybenzenediazonlum tetrafluoroborate (MBDF) fixed on the μTAD to produce tangerine compounds. The inherent colour variation was captured by a smartphone and processed to calculate the intensity (I). Under the optimal conditions, the limit of quantification was within 0.020-0.027 mg kg^-1. The recovery was varied in the range from 92.3% to 105.9% with a relative standard deviation (RSD) below 5%. The developed method provides an alternative strategy to extract and detect pesticides for food samples.

Color Space Transformation-Based Algorithm for Evaluation of Thermochromic Behavior of Cholesteric Liquid Crystals Using Polarized Light Microscopy

Cholesteryl ester liquid crystals exhibit thermochromic properties related to the existence of a twisted nematic phase. When used in applications such as thermal mapping, a color change is often monitored by video cameras. Thus, quantitative methods to evaluate thermochromic behavior (e.g., blue-start, red-start, red-end, color play and bandwidth) from video analysis are desirable. However, obtaining quantitative color measurements from digital images remains a significant technical challenge, especially for highly reflective samples such as liquid crystals (for which ultraviolet-visible (UV-vis) reflectance spectroscopy is typically used). We developed a method to determine thermochromic properties from videos of liquid crystal cooling under polarized light microscopy. We relate observed color transitions to quantifiable changes in the cumulative color difference in the International Commission on Illumination (CIE) L*a*b* color space and validate this method with UV-vis reflectance spectroscopy. The measured thermochromic behavior and associated measurement uncertainties (coefficient of variations) were comparable to UV-vis reflectance measurements.

A smartphone-based biomedical sensory system

Disease diagnostics, food safety monitoring and environmental quality monitoring are the key means to safeguard human health. However, conventional detection devices for health care are costly, bulky and complex, restricting their applications in resource-limited areas of the world. With the rapid development of biosensors and the popularization of smartphones, smartphone-based sensing systems have emerged as novel detection devices that combine the sensitivity of biosensors and diverse functions of smartphones to provide a rapid, low-cost and convenient detection method. In these systems, a smartphone is used as a microscope to observe and count cells, as a camera to record fluorescence images, as an analytical platform to analyze experimental data, and as an effective tool to connect detection devices and online doctors. These systems are widely used for cell analysis, biochemical analysis, immunoassays, and molecular diagnosis, which are applied in the fields of disease diagnostics, food safety monitoring and environmental quality monitoring. Therefore, we discuss four types of smartphone-based sensing systems in this review paper, specifically in terms of the structure, performance and efficiency of these systems. Finally, we give some suggestions for improvement and future prospective trends.