ArticleLab on a chip2024
Artificial intelligence performance in testing microfluidics for point-of-care.
Article in Lab on a chip, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 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
6 citing papers in PubMed.
- From biomimicry to clinical actionability: rethinking high-shear thrombosis as a mechanobiological system.Current opinion in hematology · 2026Review
- Smart microfluidic devices integrated in electrochemical point-of-care platforms for biomarker detection in biological fluids.Analytical and bioanalytical chemistry · 2026Review
- Label-free phenotypic antimicrobial susceptibility testing on microfluidic platforms: a review of advances and translation.Mikrochimica acta · 2025Review
- AI-Enabled Microfluidics for Respiratory Pathogen Detection.Sensors (Basel, Switzerland) · 2025Review
- Advancing clinical biochemistry: addressing gaps and driving future innovations.Frontiers in medicine · 2025Review
- Machine Learning-Driven Innovations in Microfluidics.Biosensors · 2024Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
Abstract
Artificial intelligence (AI) is revolutionizing medicine by automating tasks like image segmentation and pattern recognition. These AI approaches support seamless integration with existing platforms, enhancing diagnostics, treatment, and patient care. While recent advancements have demonstrated AI superiority in advancing microfluidics for point of care (POC) diagnostics, a gap remains in comparative evaluations of AI algorithms in testing microfluidics. We conducted a comparative evaluation of AI models specifically for the two-class classification problem of identifying the presence or absence of bubbles in microfluidic channels under various imaging conditions. Using a model microfluidic system with a single channel loaded with 3D transparent objects (bubbles), we challenged each of the tested machine learning (ML) (
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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.