AIMC Topic: Lab-On-A-Chip Devices
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In-vitro blood purification using tiny pinch holographic optical tweezers based on deep learning.
Biosensors & bioelectronics
Sep 12, 2024
In-vitro blood purification is essential to a wide range of medical treatments, requiring fine-grained analysis and precise separation of blood components. Despite existing methods that can extract specific components from blood by size or by magneti...
An artificial intelligence handheld sensor for direct reading of nickel ion and ethylenediaminetetraacetic acid in food samples using ratiometric fluorescence cellulose paper microfluidic chip.
International journal of biological macromolecules
Aug 30, 2024
User-friendly in-field sensing protocol is crucial for the effective tracing of intended analytes under less-developed countries or resources-limited environments. Nevertheless, existing sensing strategies require professional technicians and expensi...
High-Throughput and Integrated CRISPR/Cas12a-Based Molecular Diagnosis Using a Deep Learning Enabled Microfluidic System.
ACS nano
Aug 22, 2024
CRISPR/Cas-based molecular diagnosis demonstrates potent potential for sensitive and rapid pathogen detection, notably in SARS-CoV-2 diagnosis and mutation tracking. Yet, a major hurdle hindering widespread practical use is its restricted throughput,...
Role of Machine Learning Assisted Biosensors in Point-of-Care-Testing For Clinical Decisions.
ACS sensors
Aug 15, 2024
Point-of-Care-Testing (PoCT) has emerged as an essential component of modern healthcare, providing rapid, low-cost, and simple diagnostic options. The integration of Machine Learning (ML) into biosensors has ushered in a new era of innovation in the ...
Integrating machine learning and biosensors in microfluidic devices: A review.
Biosensors & bioelectronics
Aug 3, 2024
Microfluidic devices are increasingly widespread in the literature, being applied to numerous exciting applications, from chemical research to Point-of-Care devices, passing through drug development and clinical scenarios. Setting up these microenvir...
Advances in AI-assisted biochip technology for biomedicine.
Biomedicine & pharmacotherapy = Biomedecine & pharmacotherapie
Jun 28, 2024
The integration of biochips with AI opened up new possibilities and is expected to revolutionize smart healthcare tools within the next five years. The combination of miniaturized, multi-functional, rapid, high-throughput sample processing and sensin...
Deep learning unlocks label-free viability assessment of cancer spheroids in microfluidics.
Lab on a chip
Jun 11, 2024
Despite recent advances in cancer treatment, refining therapeutic agents remains a critical task for oncologists. Precise evaluation of drug effectiveness necessitates the use of 3D cell culture instead of traditional 2D monolayers. Microfluidic plat...
GNN-Based Concentration Prediction With Variable Input Flow Rates for Microfluidic Mixers.
IEEE transactions on biomedical circuits and systems
May 28, 2024
Recent years have witnessed significant advances brought by microfluidic biochips in automating biochemical protocols. Accurate preparation of fluid samples is an essential component of these protocols, where concentration prediction and generation a...
Screening for urothelial carcinoma cells in urine based on digital holographic flow cytometry through machine learning and deep learning methods.
Lab on a chip
May 14, 2024
The incidence of urothelial carcinoma continues to rise annually, particularly among the elderly. Prompt diagnosis and treatment can significantly enhance patient survival and quality of life. Urine cytology remains a widely-used early screening meth...
Combining deep learning and droplet microfluidics for rapid and label-free antimicrobial susceptibility testing of colistin.
Biosensors & bioelectronics
Apr 16, 2024
Efficient tools for rapid antibiotic susceptibility testing (AST) are crucial for appropriate use of antibiotics, especially colistin, which is now often considered a last resort therapy with extremely drug resistant Gram-negative bacteria. Here, we ...
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