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Artificial intelligence performance in testing microfluidics for point-of-care.

Mert Tunca Doganay1, Purbali Chakraborty1, Sri Moukthika Bommakanti1

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Artificial intelligence (AI) models were compared for detecting bubbles in microfluidic channels. A random forest model excelled in machine learning, while DenseNet169 showed superior performance for deep learning in point-of-care diagnostics.

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Area of Science:

  • Medical technology
  • Artificial intelligence in diagnostics
  • Microfluidics for healthcare

Background:

  • Artificial intelligence (AI) is transforming medicine, particularly in diagnostics and patient care.
  • AI excels in tasks like image segmentation and pattern recognition, integrating with existing healthcare platforms.
  • While AI shows promise in microfluidics for point-of-care (POC) diagnostics, comparative studies of AI algorithms for microfluidic testing are lacking.

Purpose of the Study:

  • To comparatively evaluate machine learning (ML) and deep learning (DL) AI models for bubble detection in microfluidic channels.
  • To identify the best-performing AI algorithms for microfluidic testing under various imaging conditions.
  • To assess the potential of AI, specifically DL models, for mobile POC diagnostic applications.

Main Methods:

  • A model microfluidic system with a single channel containing 3D transparent objects (bubbles) was used.
  • Six ML and nine DL models were tested across different background settings to classify the presence or absence of bubbles.
  • Performance was evaluated using sensitivity, specificity, and Area Under the Curve (AUC).

Main Results:

  • The random forest ML model achieved 95.52% sensitivity, 82.57% specificity, and 97% AUC, outperforming other ML algorithms.
  • DenseNet169, a DL model suitable for mobile integration, demonstrated 92.63% sensitivity, 92.22% specificity, and 92% AUC.
  • DenseNet169 integrated into a mobile POC system achieved high accuracy (>0.84) in challenging microfluidic testing conditions.

Conclusions:

  • AI holds significant potential to revolutionize precision medicine through accurate and accessible diagnostics.
  • The study highlights the effectiveness of specific AI models, like random forest and DenseNet169, for microfluidic bubble detection.
  • Integrating AI into healthcare systems can enhance patient outcomes and streamline diagnostic processes, especially in POC settings.