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

ABSTRACT: 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 experiments remains constrained by the labour-intensive, time-consuming, and inconsistent nature of manual fluid path tracking. Here, we present AI-CMCA, an artificial intelligence framework designed for capillary microfluidic chip analysis, which automates fluid path detection and tracking using deep learning-based segmentation. AI-CMCA combines transfer learning-based feature initialization, encoder-decoder-based semantic segmentation to recognize fluid in each frame, and sequential frame analysis to track then quantify fluid progression. Among the fiv

SUBMITTER: Khalghollah M 

PROVIDER: S-EPMC12280094 | biostudies-literature | 2025 Jul

REPOSITORIES: biostudies-literature