Science Translational Medicine, has published research from one of our Pitt faculty, Dr. Chakra Chennubhotla, and his former graduate student, Dr. Shannon Quinn.
Shannon P. Quinn, Maliha J. Zahid, John R. Durkin, Richard J. Francis, Cecilia W. Lo, and S. Chakra Chennubhotla (2015) “Automated identification of abnormal respiratory ciliary motion in nasal biopsies” Science Translational Medicine Vol. 7, Issue 299, pp. 299ra124.
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 an 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 over 90% of patients. Such a “black box” method will allow untrained medical professionals to sensitively diagnose challenging ciliopathies.