AIMC Topic: Microfluidics

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Machine learning and microfluidic integration for oocyte quality prediction.

Scientific reports
Despite advancements in in vitro fertilization (IVF) over the past 30 years, its outcome effectiveness remains low (20-40%). This study introduces a microfluidic-based machine learning framework to improve predictive accuracy in oocyte quality assess...

AI-CMCA: a deep learning-based segmentation framework for capillary microfluidic chip analysis.

Scientific reports
Capillary microfluidic chips (CMCs) enable passive liquid transport via surface tension and wettability gradients, making them central to point-of-care diagnostics and biomedical sensing. However, accurate analysis of capillary-driven flow experiment...

Permanent magnetic droplet-derived microrobots.

Science advances
Microrobots hold substantial potential for precision medicine. However, challenges remain in balancing multifunctional cargo loading with efficient locomotion and in predicting behavior in complex biological environments. Here, we present permanent m...

Deep Learning-Enhanced Hand-Driven Spatial Encoding Microfluidics for Multiplexed Molecular Testing at Home.

ACS nano
The frequent global outbreaks of viral infectious diseases have significantly heightened the urgent demand for molecular testing at home. However, the labor-intensive sample preparation and nucleic acid amplification steps, along with the complexity ...

An integrated microfluidic and fluorescence platform for probing in vivo neuropharmacology.

Neuron
Neurotechnologies and genetic tools for dissecting neural circuit functions have advanced rapidly over the past decade although the development of complementary pharmacological methodologies has comparatively lagged. Understanding the precise pharmac...

Advancements in Circulating Tumor Cell Detection for Early Cancer Diagnosis: An Integration of Machine Learning Algorithms with Microfluidic Technologies.

Biosensors
Circulating tumor cells (CTCs) are vital indicators of metastasis and provide a non-invasive method for early cancer diagnosis, prognosis, and therapeutic monitoring. However, their low prevalence and heterogeneity in the bloodstream pose significant...

Reducing hepatitis C diagnostic disparities with a fully automated deep learning-enabled microfluidic system for HCV antigen detection.

Science advances
Viral hepatitis remains a major global health issue, with chronic hepatitis B (HBV) and hepatitis C (HCV) causing approximately 1 million deaths annually, primarily due to liver cancer and cirrhosis. More than 1.5 million people contract HCV each yea...

Automated Electrical Detection of Proteins for Oral Squamous Cell Carcinoma in an Integrated Microfluidic Chip Using Multi-Frequency Impedance Cytometry and Machine Learning.

Sensors (Basel, Switzerland)
Proteins can act as suitable biomarkers for the prognosis and diagnosis of certain conditions and can help us gain an understanding of the fundamental processes that occur inside an organism. In this work, we present a fully automated machine learnin...

Microfluidics with Machine Learning for Biophysical Characterization of Cells.

Annual review of analytical chemistry (Palo Alto, Calif.)
Understanding the biophysical properties of cells is essential for biological research, diagnostics, and therapeutics. Microfluidics enhances biophysical cell characterization by enabling precise manipulation and real-time measurement at the microsca...

Intelligent Microfluidics for Plasma Separation: Integrating Computational Fluid Dynamics and Machine Learning for Optimized Microchannel Design.

Biosensors
Efficient separation of blood plasma and Packed Cell Volume (PCV) is vital for rapid blood sensing and early disease detection, especially in point-of-care and resource-limited environments. Conventional centrifugation methods for separation are reso...