Source Nature · Published · Open the original ↗
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Machine learning analysis of cilia driven particle transport distinguishes primary ciliary dyskinesia from normal cilia
We developed CiliaTracks, a machine learning framework that analyses cilia-driven particle transport on cultured airway cells from high-speed microscopy. It distinguishes primary ciliary dyskinesia from normal samples with 95-97% accuracy.
Primary ciliary dyskinesia (PCD) is a genetic condition causing dysmotile cilia and abnormal mucociliary clearance. Despite advances in understanding disease pathogenesis, diagnosis remains challenging. Here, we show that machine learning analysis of cilia-driven particle transport can objectively detect primary ciliary dyskinesia-related dysfunction. Using high-speed video microscopy, we captured fluorescent microsphere movement on cultured multiciliated cells from 14 individuals with primary ciliary dyskinesia and 10 healthy donors (602 videos). We developed CiliaTracks, an open-source computational framework that extracts measures of micropsphere movement, visualizes transport patterns, and classifies cilia-propelled particle transport. Quantitative and visual analyses reveal distinct motility profiles between PCD and non-primary ciliary dyskinesia and normal recordings. […]
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