Machine learning analysis of cilia driven particle transport distinguishes primary ciliary dyskinesia from normal cilia
Abstract
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. Classical machine learning models and a convolutional neural network classify recordings with 95-97% accuracy and differentiate affected from normal samples. Machine learning provides an objective, accurate framework for characterizing ciliary dysfunction, offering potential as a diagnostic tool for primary ciliary dyskinesia.
Related
- Modelling nano-particle-PEG-NLS complexes for nucleus-targeted drug delivery: revisiting the multi-dynein nano-cargo transport on microtubules
- Making the Diagnosis of Primary Ciliary Dyskinesia in Australia
- Impulse control disorder is linked to dyskinesia development through a cortical network in Parkinson’s disease
- Lurasidone Induced Amphibian Like Buccal Oscillations: A Rare Case of Early Onset Tardive Dyskinesia Like Disorder in a Geriatric Patient with Treatment-Resistant Depression
- Uncertainty-aware physics-informed meta-learning for predicting niosomal particle size across formulations and processing conditions
BibTeX
@article{hadas2026machine,
title = {{Machine learning analysis of cilia driven particle transport distinguishes primary ciliary dyskinesia from normal cilia}},
author = {Nicholas Hadas and Huihui Xu and Shambhawee Neupane and Wang K. Twan and Ahmed Elgamal and Andrew Lunel and Moe R. Mahjoub and Amjad Horani},
journal = {Communications Biology},
year = {2026},
month = oct,
doi = {10.1038/s42003-026-11149-0},
url = {https://www.nature.com/articles/s42003-026-11149-0},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - Machine learning analysis of cilia driven particle transport distinguishes primary ciliary dyskinesia from normal cilia
AU - Nicholas Hadas
AU - Huihui Xu
AU - Shambhawee Neupane
AU - Wang K. Twan
AU - Ahmed Elgamal
AU - Andrew Lunel
AU - Moe R. Mahjoub
AU - Amjad Horani
PY - 2026
DA - 2026/10/12
JO - Communications Biology
DO - 10.1038/s42003-026-11149-0
UR - https://www.nature.com/articles/s42003-026-11149-0
AB - 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. Classical machine learning models and a convolutional neural network classify recordings with 95-97% accuracy and differentiate affected from normal samples. Machine learning provides an objective, accurate framework for characterizing ciliary dysfunction, offering potential as a diagnostic tool for primary ciliary dyskinesia.
SN - 2399-3642
ER -