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Facial nerve paralysis and other conditions can have an aesthetic impact on facial dynamics by altering the movement and symmetry of facial expressions. The current methods for analyzing facial dynamics utilize either time-consuming manual landmarking of videos, or expensive and cumbersome 4D technology that is not readily available to practicing providers. Our team aimed to find a more convenient and affordable method to analyze facial dynamics by utilizing machine learning algorithms to automatically landmark and track facial features in 2D videos, including easily obtainable smartphone videos. The developed tools offer immediate quantitative measures of aesthetics and functional facial characteristics, which provide clinicians a useful tool for improving the perioperative assessment and targeted treatment for patients with facial paralysis and other facial abnormalities. Machine Learning Tool to Track and Assess Facial Dynamics in 2D Videos Bar Y. Ainuz, BS; Jeyna K. Perez, MS; James R. Seaward, MD; Alex A. Kane, MD; Rami R. Hallac, PhD Division of Plastic and Reconstructive Surgery, University of Texas Southwestern, Dallas Analytical Imaging and Modeling Center, Childrens Health Systems of Texas, Dallas, Texas

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