ArticleAdvanced science (Weinheim, Baden-Wurttemberg, Germany)2025
Deep Learning-Assisted Label-Free Parallel Cell Sorting with Digital Microfluidics.
Article in Advanced science (Weinheim, Baden-Wurttemberg, Germany), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
10 citing papers in PubMed.
- Multi-mode droplet splitting on active-matrix digital microfluidics: quantitative boundaries and optimal sequential generation.Microsystems & nanoengineering · 2026Article
- Functional drug screening of tumor organoids on an active-matrix digital microfluidic chip for cancer precision medicine.Microsystems & nanoengineering · 2026Article
- AI-Assisted Molecular Biosensors: Design Strategies for Wearable and Real-Time Monitoring.International journal of molecular sciences · 2026Review
- Convergent bioprinting and microfluidics: toward next-generation biomimetic tumor models.Hepatobiliary surgery and nutrition · 2026Article
- Optoelectronic Tweezers for Single-Cell Research: Principles, Applications, and Prospects.Cyborg and bionic systems (Washington, D.C.) · 2026Review
- Advances in machine learning-enhanced microfluidic cell sorting.Science advances · 2025Review
- Transforming microfluidics for single-cell analysis with robotics and artificial intelligence.Lab on a chip · 2025Review
- Nucleic acid amplification tests in digital microfluidics: the promise of next-generation point-of-care diagnostics.Microsystems & nanoengineering · 2025Review
- Intelligent single-cell manipulation: LLMs- and object detection-enhanced active-matrix digital microfluidics.Microsystems & nanoengineering · 2025Article
- Deep Learning-Assisted Label-Free Parallel Cell Sorting with Digital Microfluidics.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
13 authors.
Funding
Abstract
Sorting specific cells from heterogeneous samples is important for research and clinical applications. In this work, a novel label-free cell sorting method is presented that integrates deep learning image recognition with microfluidic manipulation to differentiate cells based on morphology. Using an Active-Matrix Digital Microfluidics (AM-DMF) platform, the YOLOv8 object detection model ensures precise droplet classification, and the Safe Interval Path Planning algorithm manages multi-target, collision-free droplet path planning. Simulations and experiments revealed that detection model precision, concentration ratios, and sorting cycles significantly affect recovery rates and purity. With HeLa cells and polystyrene beads as samples, the method achieved 98.5% sorting precision, 96.49% purity, and an 80% recovery over three cycles. After a series of experimental validations, this method can also be used to sort HeLa cells from red blood cells, cancer cells from white blood cells (represented by HeLa and Jurkat cells), and differentiate white blood cell subtypes (represented by HL-60 cells and Jurkat cells). Cells sorted using this method can be lysed directly on chip within their hosting droplets, ensuring minimal sample loss and suitability for downstream bioanalysis. This innovative AM-DMF cell sorting technique holds significant potential to advance diagnostics, therapeutics, and fundamental research in cell biology.
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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 Socratic graph.