ReviewBiomicrofluidics2020
Machine learning-enabled multiplexed microfluidic sensors.
Review in Biomicrofluidics, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 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
17 citing papers in PubMed.
- POC Sensor Systems and Artificial Intelligence-Where We Are Now and Where We Are Going?Biosensors · 2025Review
- Article
- Machine Learning-Driven Innovations in Microfluidics.Biosensors · 2024Review
- AI-Based Metamaterial Design.ACS applied materials & interfaces · 2024Review
- Deep learning-augmented T-junction droplet generation.iScience · 2024Article
- Machine Learning-Enabled Optimization of Interstitial Fluid Collection via a Sweeping Microneedle Design.ACS omega · 2023Article
- Advances in Integration, Wearable Applications, and Artificial Intelligence of Biomedical Microfluidics Systems.Micromachines · 2023Review
- Microsystem Advances through Integration with Artificial Intelligence.Micromachines · 2023Review
- Modular microfluidics for life sciences.Journal of nanobiotechnology · 2023Review
- Shape Fidelity Evaluation of Alginate-Based Hydrogels through Extrusion-Based Bioprinting.Journal of functional biomaterials · 2022Article
- 3D-printed microrobots from design to translation.Nature communications · 2022Review
- Disposable paper-based microfluidics for fertility testing.iScience · 2022Review
- Toilet-based continuous health monitoring using urine.Nature reviews. Urology · 2022Review
- Deep Learning-Enabled Technologies for Bioimage Analysis.Micromachines · 2022Review
- Intelligent acoustofluidics enabled mini-bioreactors for human brain organoids.Lab on a chip · 2021Article
- Glioma-on-a-Chip Models.Micromachines · 2021Article
- Hemp-Based Microfluidics.Micromachines · 2021Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
High-throughput, cost-effective, and portable devices can enhance the performance of point-of-care tests. Such devices are able to acquire images from samples at a high rate in combination with microfluidic chips in point-of-care applications. However, interpreting and analyzing the large amount of acquired data is not only a labor-intensive and time-consuming process, but also prone to the bias of the user and low accuracy. Integrating machine learning (ML) with the image acquisition capability of smartphones as well as increasing computing power could address the need for high-throughput, accurate, and automatized detection, data processing, and quantification of results. Here, ML-supported diagnostic technologies are presented. These technologies include quantification of colorimetric tests, classification of biological samples (cells and sperms), soft sensors, assay type detection, and recognition of the fluid properties. Challenges regarding the implementation of ML methods, including the required number of data points, image acquisition prerequisites, and execution of data-limited experiments are also discussed.
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.