ArticleAnalytical chemistry2025
Smartphone-Powered Automated Image Recognition Tool for Multianalyte Rapid Tests: Application to Infectious Diseases.
Article in Analytical chemistry, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 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
5 citing papers in PubMed.
- Machine learning-assisted nanozyme nanosensor for instantaneous quantitative analysis of antitubercular drugs.Journal of nanobiotechnology · 2026Article
- Portable Raspberry Pi Platform for Automated Interpretation of Lateral Flow Strip Tests.Sensors (Basel, Switzerland) · 2026Article
- Emerging Trends in Artificial Intelligence-Assisted Colorimetric Biosensors for Pathogen Diagnostics.Sensors (Basel, Switzerland) · 2026Review
- Development and Evaluation of a Smartphone App-Based Rapid 25-Hydroxy Vitamin D Test.Diagnostics (Basel, Switzerland) · 2025Article
- Point of Care Testing, Rapid Next Generation Sequencing and Artificial Intelligence in Pediatric and Neonatal Healthcare: A Narrative Review.Pharmaceuticals (Basel, Switzerland) · 2025Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Point-of-Care Testing (POCT) is rapidly increasing, providing quick, user-friendly, and portable diagnostic tools. Lateral flow assays (LFAs) have been central to POCT, administering fast and cost-effective diagnosis. However, traditional LFAs are limited to qualitative or semiquantitative results. The integration of artificial intelligence (AI) and image analysis with LFAs has significantly improved diagnostic accuracy, result automation, and quantification where applicable. ΑΙ/image analysis algorithms are trained to automatically correlate the visual results with the presence of the analyte in the sample. Smartphone-based devices increase accessibility but also face challenges such as strip positioning and background lighting, which image analysis can potentially address. This study demonstrates a smartphone and machine vision-driven multicolor LFA, as well as an additional independent AI tool, for detecting pathogens like
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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.