ArticleSensors and actuators. B, Chemical2019
Point-of-care Colorimetric Analysis through Smartphone Video.
Article in Sensors and actuators. B, Chemical, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 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
16 citing papers in PubMed.
- Structure guided design and molecular modelling of a novel peptide-SWCNT biosensor targeting NS1 dengue virus.RSC advances · 2025Article
- Environmental Determinants of Dengue Fever: A Re-Emerging Threat in the Middle East.Health science reports · 2025Article
- Development of a smartphone enabled, paper-based quantitative diagnostic assay using the HueDx color correction system.PloS one · 2024Article
- Microfluidic Devices for HIV Diagnosis and Monitoring at Point-of-Care (POC) Settings.Biosensors · 2022Review
- A free customizable tool for easy integration of microfluidics and smartphones.Scientific reports · 2022Article
- Improving the Quantification of Colorimetric Signals in Paper-Based Immunosensors with an Open-Source Reader.Sensors (Basel, Switzerland) · 2022Article
- Dengue Detection: Advances in Diagnostic Tools from Conventional Technology to Point of Care.Biosensors · 2021Review
- Diagnosis for COVID-19: current status and future prospects.Expert review of molecular diagnostics · 2021Review
- High-Performance Estimation of Lead Ion Concentration Using Smartphone-Based Colorimetric Analysis and a Machine Learning Approach.ACS omega · 2020Article
- Smartphone-based sickle cell disease detection and monitoring for point-of-care settings.Biosensors & bioelectronics · 2020Article
- Article
- Development of a Flow-Free Automated Colorimetric Detection Assay Integrated with Smartphone for Zika NS1.Diagnostics (Basel, Switzerland) · 2020Article
- Microfluidic Chip for Detection of Fungal Infections.ACS omega · 2019Article
- Cell phone based colorimetric analysis for point-of-care settings.The Analyst · 2019Article
- Development of a Flow-Free Magnetic Actuation Platform for an Automated Microfluidic ELISA.RSC advances · 2019Article
- Management of COVID-19: current status and future prospects.Microbes and infectionReview
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
Abstract
Point-of-care (POC) tests often rely on smartphone image methods for colorimetric analysis, but the results of such methods are frequently difficult to reproduce or standardize. The problem is aggravated by unpredictable image capture conditions, which pose a significant challenge when low limits of detection (LOD) are needed. Application-specific smartphone attachments are often used to standardize imaging conditions, but there has recently been an interest in equipment-free POC colorimetric analysis. Improved output metrics and preprocessing methods have been developed, but equipment-free imaging often has a high LOD and is inappropriate for quantitative tasks. Additional work is necessary to replace external smartphone attachments with algorithms. Towards this end, we have developed a video processing method that synthesizes many images into a single output metric. We use image features to select the best inputs from a large set of video frames and demonstrate that the resulting output values have a stronger correlation with laboratory methods and a lower standard error. The developed algorithm only requires 20 seconds of video and can easily be integrated with existing processing methods. We apply our algorithm to the NS1-based sandwich ELISA for Zika detection and show that the LOD is two times lower when our video-based method is used.
Indexed as
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.