ReviewFrontiers in bioengineering and biotechnology2023
Deep learning with microfluidics for on-chip droplet generation, control, and analysis.
Review in Frontiers in bioengineering and biotechnology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 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
8 citing papers in PubMed.
- Droplet Digital CRISPR for Nucleic Acid Detection.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Review
- Microdroplet Systems for Gene Transfer: From Fundamentals to Future Perspectives.Micromachines · 2025Review
- Advancements and Future Perspectives of Microfluidic Technology in Pediatric Healthcare.Smart medicine · 2025Review
- Design and Implementation of a High-Throughput Digital Microfluidic System Based on Optimized YOLOv8 Object Detection.Micromachines · 2025Article
- Data-driven models for microfluidics: A short review.Biomicrofluidics · 2024Review
- Emerging Trends in Integrated Digital Microfluidic Platforms for Next-Generation Immunoassays.Micromachines · 2024Review
- Next-Generation Microfluidics for Biomedical Research and Healthcare Applications.Biomedical engineering and computational biology · 2023Review
- Droplet-based methodology for investigating bacterial population dynamics in response to phage exposure.Frontiers in microbiology · 2023Article
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
Droplet microfluidics has gained widespread attention in recent years due to its advantages of high throughput, high integration, high sensitivity and low power consumption in droplet-based micro-reaction. Meanwhile, with the rapid development of computer technology over the past decade, deep learning architectures have been able to process vast amounts of data from various research fields. Nowadays, interdisciplinarity plays an increasingly important role in modern research, and deep learning has contributed greatly to the advancement of many professions. Consequently, intelligent microfluidics has emerged as the times require, and possesses broad prospects in the development of automated and intelligent devices for integrating the merits of microfluidic technology and artificial intelligence. In this article, we provide a general review of the evolution of intelligent microfluidics and some applications related to deep learning, mainly in droplet generation, control, and analysis. We also present the challenges and emerging opportunities in this field.
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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.