ArticleMicrosystems & nanoengineering2024
An artificial intelligence-assisted digital microfluidic system for multistate droplet control.
Article in Microsystems & nanoengineering, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
What it found
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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.
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Who cites it
14 citing papers in PubMed.
- Integrated microfluidic biosensors: shaping the future of quantitative life sciences and on-chip molecular diagnostics.Lab on a chip · 2026Review
- Raman spectroscopy combined with multiple technologies for label-free identification of immune cells: An overview.Journal of pharmaceutical analysis · 2026Review
- Peroxidase-Mimicking Nanozymes for Rapid Detection of Infectious Diseases.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Review
- Microfluidic point-of-care testing for antimicrobial resistance: advances, strategies, and One Health perspectives.Science in One Health · 2026Review
- From microfluidics to nanodelivery: artificial intelligence reshapes neuropharmacology research strategies.Frontiers in pharmacology · 2026Review
- Advances in machine learning-enhanced microfluidic cell sorting.Science advances · 2025Review
- A high-precision nanoliter droplet dispensing system based on optoelectrowetting with tunable droplet volume.Microsystems & nanoengineering · 2025Article
- Transforming microfluidics for single-cell analysis with robotics and artificial intelligence.Lab on a chip · 2025Review
- Hydrogel Microspheres as Versatile Platforms for Biomedical Research: Design, Properties, and Applications.MedComm · 2025Review
- Droplet Generation and Manipulation in Microfluidics: A Comprehensive Overview of Passive and Active Strategies.Biosensors · 2025Review
- Design and Implementation of a High-Throughput Digital Microfluidic System Based on Optimized YOLOv8 Object Detection.Micromachines · 2025Article
- Advancements of paper-based microfluidics and organ-on-a-chip models in cosmetics hazards.RSC advances · 2025Review
- Application of Digital Polymerase Chain Reaction (dPCR) in Non-Invasive Prenatal Testing (NIPT).Biomolecules · 2025Review
- Innovative Advances in Droplet Microfluidics.Research (Washington, D.C.) · 2025Review
Corrections and comments
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Authors and funding
7 authors.
Funding
Abstract
Digital microfluidics (DMF) is a versatile technique for parallel and field-programmable control of individual droplets. Given the high level of variability in droplet manipulation, it is essential to establish self-adaptive and intelligent control methods for DMF systems that are informed by the transient state of droplets and their interactions. However, most related studies focus on droplet localization and shape recognition. In this study, we develop the AI-assisted DMF framework μDropAI for multistate droplet control on the basis of droplet morphology. The semantic segmentation model is integrated into our custom-designed DMF system to recognize the droplet states and their interactions for feedback control with a state machine. The proposed model has strong flexibility and can recognize droplets of different colors and shapes with an error rate of less than 0.63%; it enables control of droplets without user intervention. The coefficient of variation (CV) of the volumes of split droplets can be limited to 2.74%, which is lower than the CV of traditional dispensed droplets, contributing to an improvement in the precision of volume control for droplet splitting. The proposed system inspires the development of semantic-driven DMF systems that can interface with multimodal large language models (MLLMs) for fully automatic control.
Identifiers
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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.