Research ArticleComputational Biology

Automated identification of abnormal respiratory ciliary motion in nasal biopsies

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Science Translational Medicine  05 Aug 2015:
Vol. 7, Issue 299, pp. 299ra124
DOI: 10.1126/scitranslmed.aaa1233

Cilia in motion

The movement of tiny cilia can be used to detect various lung and heart diseases. Normally, these cilia beat in unison to move foreign particles and mucus out of the body. When diseased, the cilia adopt asynchronous motions, which can be observed in nasal or bronchial biopsies under a microscope and in turn be used for diagnosis. To reduce the subjective nature of diagnostics involving manual evaluation of ciliary motion, Quinn et al. devised a computational framework that objectively quantifies ciliary motion in digital biopsy videos. In their approach, ciliary motion is characterized as a “dynamic texture,” much like a flickering flame or billowing smoke. The ciliary motion was broken down into elemental components, which were then pieced together to create a digital “signature” capturing cilia rotation and deformation as functions of time and magnitude. Using these digital signatures, the authors were able to formulate ciliary motion predictions for two independent cohorts from different institutions that included patients with primary ciliary dyskinesia, congenital heart disease, and heterotaxy. Their computational framework was able to correctly identify ciliary motion defects in more than 90% of patients. Such a “black box” method will allow untrained medical professionals to sensitively diagnose challenging ciliopathies.


Motile cilia lining the nasal and bronchial passages beat synchronously to clear mucus and foreign matter from the respiratory tract. This mucociliary defense mechanism is essential for pulmonary health, because respiratory ciliary motion defects, such as those in patients with primary ciliary dyskinesia (PCD) or congenital heart disease, can cause severe sinopulmonary disease necessitating organ transplant. The visual examination of nasal or bronchial biopsies is critical for the diagnosis of ciliary motion defects, but these analyses are highly subjective and error-prone. Although ciliary beat frequency can be computed, this metric cannot sensitively characterize ciliary motion defects. Furthermore, PCD can present without any ultrastructural defects, limiting the use of other detection methods, such as electron microscopy. Therefore, an unbiased, computational method for analyzing ciliary motion is clinically compelling. We present a computational pipeline using algorithms from computer vision and machine learning to decompose ciliary motion into quantitative elemental components. Using this framework, we constructed digital signatures for ciliary motion recognition and quantified specific properties of the ciliary motion that allowed high-throughput classification of ciliary motion as normal or abnormal. We achieved >90% classification accuracy in two independent data cohorts composed of patients with congenital heart disease, PCD, or heterotaxy, as well as healthy controls. Clinicians without specialized knowledge in machine learning or computer vision can operate this pipeline as a “black box” toolkit to evaluate ciliary motion.

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