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Toward an Automatic System for Computer-Aided Assessment in Facial Palsy.

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Summary of "Toward an Automatic System for Computer-Aided Assessment in Facial Palsy."

Quantitative assessment of facial function is challenging, and subjective grading scales such as House-Brackmann, Sunnybrook, and eFACE have well-recognized limitations. Machine learning (ML) approaches to facial landmark localization carry great clinical potential as they enable high-throughput automated quantification of relevant facial metrics from photographs and videos. However, the translation from research settings to clinical application still requires important improvements. To develop a novel ML algorithm for fast and accurate localization of facial landmarks in photographs of facial palsy patients and utilize this technology as part of an automated computer-aided diagnosis system. Portrait photographs of 8 expressions obtained from 200 facial palsy patients and 10 healthy participants were manually annotated by localizing 68 facial landmarks in each photograph and by 3 trained clinicians using a custom graphical user interface. A novel ML model for automated facial landmark localization was trained using this disease-specific database. Algorithm accuracy was compared with manual markings and the output of a model trained using a larger database consisting only of healthy subjects. Root mean square error normalized by the interocular distance (NRMSE) of facial landmark localization between prediction of ML algorithm and manually localized landmarks. Publicly available algorithms for facial landmark localization provide poor localization accuracy when applied to photographs of patients compared with photographs of healthy controls (NRMSE, 8.56 ± 2.16 vs. 7.09 ± 2.34,  ≪ 0.01). We found significant improvement in facial landmark localization accuracy for the facial palsy patient population when using a model trained with a relatively small number photographs (1440) of patients compared with a model trained using several thousand more images of healthy faces (NRMSE, 6.03 ± 2.43 vs. 8.56 ± 2.16,  ≪ 0.01). Retraining a computer vision facial landmark detection model with fewer than 1600 annotated images of patients significantly improved landmark detection performance in frontal view photographs of this population. The new annotated database and facial landmark localization model represent the first steps toward an automatic system for computer-aided assessment in facial palsy. 4.

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This article was published in the following journal.

Name: Facial plastic surgery & aesthetic medicine
ISSN: 2689-3622
Pages: 42-49

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Medical and Biotech [MESH] Definitions

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Recurrent clonic contraction of facial muscles, restricted to one side. It may occur as a manifestation of compressive lesions involving the seventh cranial nerve (FACIAL NERVE DISEASES), during recovery from BELL PALSY, or in association with other disorders. (From Adams et al., Principles of Neurology, 6th ed, p1378)

A syndrome characterized by the acute onset of unilateral FACIAL PARALYSIS which progresses over a 2-5 day period. Weakness of the orbicularis oculi muscle and resulting incomplete eye closure may be associated with corneal injury. Pain behind the ear often precedes the onset of paralysis. This condition may be associated with HERPESVIRUS 1, HUMAN infection of the facial nerve. (Adams et al., Principles of Neurology, 6th ed, p1376)

A syndrome characterized by facial palsy in association with a herpetic eruption of the external auditory meatus. This may occasionally be associated with tinnitus, vertigo, deafness, severe otalgia, and inflammation of the pinna. The condition is caused by reactivation of a latent HERPESVIRUS 3, HUMAN infection which causes inflammation of the facial and vestibular nerves, and may occasionally involve additional cranial nerves. (From Adams et al., Principles of Neurology, 6th ed, p757)

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