High-throughput microfluidic systems accelerated by artificial intelligence for biomedical applications

Zhou, Jianhua; Dong, Jianpei; Hou, Hongwei; Huang, Lu; Li, Jinghong

doi:10.1039/d3lc01012k

Cited by 4 publications

(3 citation statements)

References 204 publications

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“…In the realm of drug discovery and disease diagnosis, AI-accelerated droplet microfl uidics demonstrates immense promise. The highthroughput nature of droplet assays, combined with AI's predictive modelling, substantially shortens the development timeline of novel therapeutics [3][4][5]. AI algorithms are revolutionizing the screening process by predicting drug effi cacy and toxicity, thus streamlining the pipeline from laboratory research to clinical trials.…”

Section: Streamlining Drug Discovery and Disease Diagnosismentioning

confidence: 99%

“…Similarly, the application of AI in analyzing microfl uidics-generated data ensures rapid and accurate diagnosis of diseases [3][4][5]. By leveraging machine learning, it is now possible to discern subtle biomarker patterns that are indicative of disease progression, accelerating the journey towards personalized medicine.…”

Section: Streamlining Drug Discovery and Disease Diagnosismentioning

confidence: 99%

“…The advent of droplet microfl uidics has revolutionized the landscape of biomedical research, off ering a platform for the precise control and manipulation of fl uids at the microscale [1,2]. Coupled with the meteoric rise of Artifi cial Intelligence (AI), these technologies present a paradigm shift in the way we approach high-throughput assays, diagnostic procedures, and the broader realm of personalized medicine [3]. This marriage of AI's analytical prowess and the functional versatility of droplet microfl uidics is poised to create a new precedence for effi ciency, accuracy, and innovation [4].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Envisioning the Future of High-Throughput Biomedical Assays through the Convergence of AI and Droplet Microfluidics

Wei,

2024

J Biomed Res Environ Sci

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Section: Streamlining Drug Discovery and Disease Diagnosismentioning

confidence: 99%

Section: Streamlining Drug Discovery and Disease Diagnosismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Envisioning the Future of High-Throughput Biomedical Assays through the Convergence of AI and Droplet Microfluidics

Wei,

2024

J Biomed Res Environ Sci

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Integrating machine learning and biosensors in microfluidic devices: A review

Antonelli,

Filippi,

D’Orazio

et al. 2024

Biosensors and Bioelectronics

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Artificial Intelligence Performance in Testing Microfluidics for Point-of-Care

Doganay,

Chakraborty,

Moukthika

et al. 2024

Preprint

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Artificial intelligence (AI) is revolutionizing medicine by automating tasks like image segmentation and pattern recognition. These AI approaches support seamless integration with existing platforms, enhancing diagnostics, treatment, and patient care. While recent advancements have demonstrated AI superiority in advancing microfluidics for point of care diagnostics, a gap remains in comparative evaluations of AI algorithms in testing microfluidics. We conducted a comparative evaluation of AI models specifically for the two-class classification problem of identifying the presence or absence of bubbles in microfluidic channels under various imaging conditions. Using a model microfluidic system with a single channel loaded with 3D transparent objects (bubbles), we challenged each of the tested machine learning (ML) (n = 6) and deep learning (DL) (n = 9) models across different background settings. Evaluation revealed that the Random Forest ML model achieved 95.52% sensitivity, 82.57% specificity, and 97% AUC, outperforming other ML algorithms. Among DL models suitable for mobile integration, DenseNet169 demonstrated superior performance, achieving 92.63% sensitivity, 92.22% specificity, and 92% AUC. Remarkably, DenseNet169 integration into a mobile POC system demonstrated exceptional accuracy (> 0.84) in testing microfluidics at under challenging imaging settings. Our study confirms the transformative potential of AI in healthcare, emphasizing its capacity to revolutionize precision medicine through accurate and accessible diagnostics. The integration of AI into healthcare systems holds promise for enhancing patient outcomes and streamlining healthcare delivery.

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High-throughput microfluidic systems accelerated by artificial intelligence for biomedical applications

Abstract: High-throughput microfluidic systems are widely used in biomedical fields for tasks like disease detection, drug testing, and material discovery. Despite the great advance in automation and throughput, the large amounts...

Cited by 4 publications

References 204 publications

Envisioning the Future of High-Throughput Biomedical Assays through the Convergence of AI and Droplet Microfluidics

Envisioning the Future of High-Throughput Biomedical Assays through the Convergence of AI and Droplet Microfluidics

Integrating machine learning and biosensors in microfluidic devices: A review

Artificial Intelligence Performance in Testing Microfluidics for Point-of-Care

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