Supplementary Material for:

Automated identification of abnormal respiratory ciliary motion in nasal biopsies

Shannon P. Quinn, Maliha J. Zahid, John R. Durkin, Richard J. Francis, Cecilia W. Lo,* S. Chakra Chennubhotla*

*Corresponding author. E-mail: cel36{at}pitt.edu (C.W.L.); chakracs{at}pitt.edu (S.C.C.)

Published 5 August 2015, Sci. Transl. Med. 7, 299ra124 (2015)
DOI: 10.1126/scitranslmed.aaa1233

This PDF file includes:

• Materials and Methods
• Fig. S1. Aggregate optical flow displacement in CHP data cohort.
• Fig. S2. Pixel selection in an ROI.
• Fig. S3. Breakdown of digital nasal biopsy video data sets.
• Fig. S4. CM classification pipeline.
• Fig. S5. Classification confidence as a function of ROIs per patient.
• Fig. S6. Web site proof-of-concept screenshots.
• Fig. S7. Pairwise angles between principal components of CM in AR models.
• Fig. S8. CM classification results of parameter scanning.
• Table S1. Constant parameters used throughout this study.
• Legends for movies S1 to S9
• Reference (42)

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Other Supplementary Material for this manuscript includes the following:

• Movie S1 (.mov format). Example of normal CM of nasal biopsy from control.
• Movie S2 (.mov format). Example of abnormal CM of nasal biopsy from PCD patient.
• Movie S3 (.mov format). Example of abnormal asynchronous and wavy CM.
• Movie S4 (.mov format). Example of abnormal CM with incomplete stroke.
• Movie S5 (.mov format). Example of abnormal CM with asynchronous beat and incomplete stroke.
• Movie S6 (.mov format). Example of a video capture artifact of extraneous tissue motion.
• Movie S7 (.mov format). Example of a video capture artifact of poor camera focus.
• Movie S8 (.mov format). Example of a false-negative prediction.
• Movie S9 (.mov format). Example of a false-positive prediction.

[Download Movies S1 to S9]