Automated identification of abnormal respiratory ciliary motion in nasal biopsies

Quinn, Shannon P., Zahid, Maliha J., Durkin, John R., Francis, Richard J., Lo, Cecilia W., and Chennubhotla, S. Chakra (2015) Automated identification of abnormal respiratory ciliary motion in nasal biopsies. Science Translational Medicine, 7 (299). 299ra124.

[img] PDF (Published Version) - Published Version
Restricted to Repository staff only

View at Publisher Website: https://doi.org/10.1126/scitranslmed.aaa...
 
20
1


Abstract

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.

Item ID: 60719
Item Type: Article (Research - C1)
ISSN: 1946-6242
Copyright Information: 2017 © The Authors, some rights reserved; exclusive licensee American Association for the Advancement of Science.
Funders: Pennsylvania Department of Health, National Institute of Health (NIH), USA
Projects and Grants: NIH Grants NIH HL-098180, NIH 1R01GM104412-01A1
Date Deposited: 23 Oct 2019 12:36
FoR Codes: 11 MEDICAL AND HEALTH SCIENCES > 1102 Cardiovascular Medicine and Haematology > 110203 Respiratory Diseases @ 50%
08 INFORMATION AND COMPUTING SCIENCES > 0801 Artificial Intelligence and Image Processing > 080109 Pattern Recognition and Data Mining @ 50%
SEO Codes: 92 HEALTH > 9202 Health and Support Services > 920203 Diagnostic Methods @ 100%
Downloads: Total: 1
More Statistics

Actions (Repository Staff Only)

Item Control Page Item Control Page