ArticleACS omega2018
Color Space Transformation-Based Smartphone Algorithm for Colorimetric Urinalysis.
Article in ACS omega, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
11 citing papers in PubMed, 55 citations in OpenAlex.
- Colorimetric Detection of Bovine Serum Albumin (BSA Protein) by Interaction and Modification of Silver Nanoparticles.ACS omega · 2025Article
- Paper-based uric acid assay in whole blood samples by ZnAnalytical and bioanalytical chemistry · 2024Article
- Machine learning-assisted image label-free smartphone platform for rapid segmentation and robust multi-urinalysis.Analytical and bioanalytical chemistry · 2024Article
- Evaluation of a smartphone-based colorimetric method for urinalysis dipstick readings in cats.Journal of feline medicine and surgery · 2023Article
- Leukocyte Esterase Strip Quantitative Detection Based on RGB Photometry is a Probable Method to Diagnose Periprosthetic Joint Infection: An Exploratory Study.Orthopaedic surgery · 2023Article
- Performance evaluation of Hipee S2 point-of-care testing urine dipstick analyser: a cross-sectional study.BMJ open · 2022Article
- Application of Janus Particles in Point-of-Care Testing.Biosensors · 2022Review
- 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.Sensors (Basel, Switzerland) · 2022Article
- A Simple and Reliable Dispersive Liquid-Liquid Microextraction with Smartphone-Based Digital Images for Determination of Carbaryl Residues inMolecules (Basel, Switzerland) · 2022Article
- High-Performance Estimation of Lead Ion Concentration Using Smartphone-Based Colorimetric Analysis and a Machine Learning Approach.ACS omega · 2020Article
- Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Urine strips are widely applied for rapid analysis of various indexes of urine for clinical examinations. The tests mainly rely on the application of a urine analyzer, which suffers several drawbacks and cannot meet the requirements of point-of-care testing (POCT). The integration of a smartphone with a biosensor has recently attracted great attention. We herein propose a human vision-based smartphone algorithm for colorimetric analysis of various urine indexes. A CIEDE2000 formula in CIELab color space is applied for the evaluation of color difference, which may greatly improve the analytical performances of urine strips. The proposed algorithm also possesses merits such as good accuracy, quantitative analysis, and limited calculation task, which is suitable for the application with smartphone platform. Experimental results demonstrate that the proposed method shows excellent reliability compared with the urine analyzer and some other algorithms. In addition, human real samples are successfully analyzed with excellent accuracy. Therefore, this work provides a convenient colorimetric tool for POCT urine analysis.
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.