Machine learning-assisted biomass colorimetric dipsticks for portable clinical nephropathy diagnosis by using smart mobile terminal

Portable and efficient means are urgently needed for diverse problems of nephropathy diagnosis in the era of the “Great health”, that is, the creatinine level identification in human fluids. Given this urgent requirement, we have developed a biomass colorimetric dipstick for creatinine determination in human fluids by using machine learning to enhance the analysis accuracy and reliability. The main active ingredients of this dipstick are safe and cheap 3,5-dinitrobenzoic acid and sodium alginate, using a facile knife coating molding, which has the potential for large-scale industrial production. Besides, the formula of colorimetric ink for printing is optimized based on a completely new reaction mechanism by plenty of control groups. As a result, the pre-fabricated colorimetric dipstick shows a wide linear detection range of 0.5 to 100 mM with the LOD at 0.18 mM for creatinine determination. As for data processing (creatinine identification), machine vision is utilized to classify the collected images and fit the accurate creatinine content through a Random Forest Regression Algorithm, thus distinguishing the condition of nephropathy. Notably, all creatinine determinations can be performed on a smartphone App based on an Android system to achieve human-computer interaction, which opens up a promising way for intelligent nephropathy diagnosis. The biomass colorimetric dipstick has achieved fast, convenient, and accurate creatinine determination with the help of machine learning under clinical conditions, showing the significant potential of intelligent nephropathy diagnosis. • First report is on colorimetric creatinine determination based on machine learning. • Safe and cheap 3,5-dinitrobenzoic acid is the main active colorimetric indicator. • DFT is used to analyze the reaction mechanism and further optimize the formulation. • Random forest and gradient boosting tree algorithm are used to improve determination accuracy. • The portable creatinine determination enables intelligent human-computer interaction.

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Publication Details

Journal
Chemical Engineering Journal
Published
2025-09-04
DOI
https://doi.org/10.1016/j.cej.2025.168059
Primary Topic
Diverse Scientific Research Studies
Type
article
Field-Weighted Citation Impact
0.00

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article

Machine learning-assisted biomass colorimetric dipsticks for portable clinical nephropathy diagnosis by using smart mobile terminal

Xinghai Liu, Rongbin Wei, Hua Shui, Bowen Zhang et al.
Chemical Engineering Journal
Diverse Scientific Research Studies
article

Machine learning-assisted biomass colorimetric dipsticks for portable clinical nephropathy diagnosis by using smart mobile terminal

Xinghai Liu, Rongbin Wei, Hua Shui, Bowen Zhang, Ding Lin, Donglei Fu, Junjie Deng, Shuaibo Zhang
article en

Abstract

Portable and efficient means are urgently needed for diverse problems of nephropathy diagnosis in the era of the “Great health”, that is, the creatinine level identification in human fluids. Given this urgent requirement, we have developed a biomass colorimetric dipstick for creatinine determination in human fluids by using machine learning to enhance the analysis accuracy and reliability. The main active ingredients of this dipstick are safe and cheap 3,5-dinitrobenzoic acid and sodium alginate, using a facile knife coating molding, which has the potential for large-scale industrial production. Besides, the formula of colorimetric ink for printing is optimized based on a completely new reaction mechanism by plenty of control groups. As a result, the pre-fabricated colorimetric dipstick shows a wide linear detection range of 0.5 to 100 mM with the LOD at 0.18 mM for creatinine determination. As for data processing (creatinine identification), machine vision is utilized to classify the collected images and fit the accurate creatinine content through a Random Forest Regression Algorithm, thus distinguishing the condition of nephropathy. Notably, all creatinine determinations can be performed on a smartphone App based on an Android system to achieve human-computer interaction, which opens up a promising way for intelligent nephropathy diagnosis. The biomass colorimetric dipstick has achieved fast, convenient, and accurate creatinine determination with the help of machine learning under clinical conditions, showing the significant potential of intelligent nephropathy diagnosis. • First report is on colorimetric creatinine determination based on machine learning. • Safe and cheap 3,5-dinitrobenzoic acid is the main active colorimetric indicator. • DFT is used to analyze the reaction mechanism and further optimize the formulation. • Random forest and gradient boosting tree algorithm are used to improve determination accuracy. • The portable creatinine determination enables intelligent human-computer interaction.

Chemical Engineering JournalVol. 522
Wuhan University (CN), Zhongnan Hospital of Wuhan University (CN), Shandong Agricultural University (CN), Wuhan Institute of Technology (CN)
Zhongnan Hospital of Wuhan University, National Key Research and Development Program of China
Openalex Percentile: Top 63%
Diverse Scientific Research Studies
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