ReviewLab on a chip2022
Machine learning for microfluidic design and control.
Review in Lab on a chip, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 49 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
49 citing papers in PubMed.
- Data-driven and mechanistic modelling of PLGA nanoparticle formation in hydrodynamic flow focusing microfluidics.Lab on a chip · 2026Article
- Integration of Artificial Intelligence and Microfluidics for Drug Delivery Applications.Micromachines · 2026Review
- Integrating Low-Voltage Microfluidics With On-Chip Electrospray Ionization for Miniaturized Protein Analysis.Small methods · 2026Review
- Integrated microfluidic biosensors: shaping the future of quantitative life sciences and on-chip molecular diagnostics.Lab on a chip · 2026Review
- Recent Advances in MEMS Actuators for Microfluidic Applications: Emerging Designs, Multiphysics Modeling, and Performance Optimization.Micromachines · 2026Review
- Autonomous microfluidic labs: progress and prospects.Lab on a chip · 2026Review
- Review
- Droplet microfluidics for biomedical applications: emerging trends and future developments.Microsystems & nanoengineering · 2026Review
- Numerical and Experimental Investigation of Mixing Enhancement in a Zigzag Passive Micromixer with D-Shaped Obstacles.Micromachines · 2026Article
- ML-automated microfluidic circuit design.Science advances · 2026Article
- CRISPR-on-Chip for Point-of-Care Diagnostics.ACS nano · 2026Review
- Translational Models for Glioblastoma: Revolutionizing Drug Development and Personalized Medicine through Clinical Insights.Theranostics · 2026Review
- Advances in machine learning-enhanced microfluidic cell sorting.Science advances · 2025Review
- Computational methods for inertial microfluidics: recent advances and future perspectives.Microsystems & nanoengineering · 2025Review
- Intelligence prediction of microfluidically prepared nanoparticles.Scientific reports · 2025Article
- HarnessingSensors (Basel, Switzerland) · 2025Review
- Article
- AI-Enabled Microfluidics for Respiratory Pathogen Detection.Sensors (Basel, Switzerland) · 2025Review
- A data driven framework for optimizing droplet microfluidics with residual block and Fourier enhanced networks.Scientific reports · 2025Article
- Machine-Learning-Aided Advanced Electrochemical Biosensors.Advanced materials (Deerfield Beach, Fla.) · 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
4 authors.
Funding
Abstract
Microfluidics has developed into a mature field with applications across science and engineering, having particular commercial success in molecular diagnostics, next-generation sequencing, and bench-top analysis. Despite its ubiquity, the complexity of designing and controlling custom microfluidic devices present major barriers to adoption, requiring intuitive knowledge gained from years of experience. If these barriers were overcome, microfluidics could miniaturize biological and chemical research for non-experts through fully-automated platform development and operation. The intuition of microfluidic experts can be captured through machine learning, where complex statistical models are trained for pattern recognition and subsequently used for event prediction. Integration of machine learning with microfluidics could significantly expand its adoption and impact. Here, we present the current state of machine learning for the design and control of microfluidic devices, its possible applications, and current limitations.
Indexed as
Identifiers
What Socratic 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 Socratic graph.