ArticleSensors (Basel, Switzerland)2019
Analyte Quantity Detection from Lateral Flow Assay Using a Smartphone.
Article in Sensors (Basel, Switzerland), 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers.
What it found
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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.
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Who cites it
22 citing papers in PubMed, 67 citations in OpenAlex.
- DeepBand: A Deep Learning-Enabled Multi-Stage Pipeline for Continuous Automated Quantification of Lateral Flow Assays.Diagnostics (Basel, Switzerland) · 2026Article
- Lateral Flow Assay to detect CXCL10 as a diagnose-supporting biomarker for Polyomavirus-associated Nephropathy (PVAN) after kidney transplantation (KTx).Scientific reports · 2026Article
- The evolution of next-generation lateral flow assays for bacterial and fungal diagnostics.Mikrochimica acta · 2026Review
- Portable Raspberry Pi Platform for Automated Interpretation of Lateral Flow Strip Tests.Sensors (Basel, Switzerland) · 2026Article
- A One-Step-Packaged CMOS/Microfluidics Wireless Bio-Analyzer for Point-of-Care Diagnostics.IEEE journal of solid-state circuits · 2025Article
- Evaluation of lateral flow devices for rabies diagnosis in decomposed animal brain samples.Tropical medicine and health · 2025Article
- Review
- Article
- Sample-to-answer platform for the clinical evaluation of COVID-19 using a deep learning-assisted smartphone-based assay.Nature communications · 2023Article
- Fluorophore-encapsulated nanobeads for on-site, rapid, and sensitive lateral flow assay.Sensors and actuators. B, Chemical · 2023Article
- Double Competitive Immunodetection of Small Analyte: Realization for Highly Sensitive Lateral Flow Immunoassay of Chloramphenicol.Biosensors · 2022Article
- Paper-based genetic assays with bioconjugated gold nanorods and an automated readout pipeline.Scientific reports · 2022Article
- Point-of-Care Monitoring of Respiratory Diseases Using Lateral Flow Assay and CMOS Camera Reader.IEEE journal of translational engineering in health and medicine · 2022Article
- Recurrence monitoring for ovarian cancer using a cell phone-integrated paper device to measure the ovarian cancer biomarker HE4/CRE ratio in urine.Scientific reports · 2021Article
- Potential of Point-of-Care and At-Home Assessment of Immune Status via Rapid Cytokine Detection and Questionnaire-Based Anamnesis.Sensors (Basel, Switzerland) · 2021Article
- A Semi-Quantitative Isothermal Diagnostic Assay Utilizing Competitive Amplification.Analytical chemistry · 2021Article
- COVID-19 Biomarkers and Advanced Sensing Technologies for Point-of-Care (POC) Diagnosis.Bioengineering (Basel, Switzerland) · 2021Review
- Has the Flood Entered the Basement? A Systematic Literature Review about Machine Learning in Laboratory Medicine.Diagnostics (Basel, Switzerland) · 2021Review
- A Cross-Disciplinary View of Testing and Bioinformatic Analysis of SARS-CoV-2 and Other Human Respiratory Viruses in Pandemic Settings.IEEE access : practical innovations, open solutions · 2021Article
- Highly portable quantitative screening test for prostate-specific antigen at point of care.Current research in biotechnology · 2021Article
Corrections and comments
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Authors and funding
5 authors at 3 institutions in 2 countries.
Funding
Abstract
Lateral flow assay (LFA) technology has recently received interest in the biochemical field since it is simple, low-cost, and rapid, while conventional laboratory test procedures are complicated, expensive, and time-consuming. In this paper, we propose a robust smartphone-based analyte detection method that estimates the amount of analyte on an LFA strip using a smartphone camera. The proposed method can maintain high estimation accuracy under various illumination conditions without additional devices, unlike conventional methods. The robustness and simplicity of the proposed method are enabled by novel image processing and machine learning techniques. For the performance analysis, we applied the proposed method to LFA strips where the target analyte is albumin protein of human serum. We use two sets of training LFA strips and one set of testing LFA strips. Here, each set consists of five strips having different quantities of albumin-10 femtograms, 100 femtograms, 1 picogram, 10 picograms, and 100 picograms. A linear regression analysis approximates the analyte quantity, and then machine learning classifier, support vector machine (SVM), which is trained by the regression results, classifies the analyte quantity on the LFA strip in an optimal way. Experimental results show that the proposed smartphone application can detect the quantity of albumin protein on a test LFA set with 98% accuracy, on average, in real time.
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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.