We thank the individual for granting permission to publish the clinical photograph used herein.

No abstract

word count: 344/350Question: Can recent developments in machine learning and computer vision be used to develop an objective and automatic system for computer-aided assessment in facial palsy? Findings:In this research article, we found that by using a relatively small number of manually annotated photographs for a patient specific database it is possible to obtain significant improvement in the accuracy of facial measurements provided by a popular machine learning algorithm. Meaning: The results presented in the article represent the first steps towards the development of an automatic system for computer-aided assessment in facial palsy. Abstract (words = 344 / 350) Importance: Quantitative assessment of facial function is challenging, and subjective grading scales such as House-Brackmann, Sunnybrook, and eFACE have well-recognized limitations. Machine learning 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. Objective: To develop a novel machine learning 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. Design, Setting, and Participants: Portrait photographs of eight expressions obtained from 200 facial palsy patients and 10 healthy participants were manually annotated, by localizing 68 facial landmarks in each photograph, by three trained clinicians using a custom graphical user interface. A novel machine learning model for automated facial landmark localization was trained using this disease-specific database. Algorithm accuracy was compared to manual markings and the output of a model trained using a larger database consisting only of healthy subjects. Main Outcomes and measurements: Root mean square error normalized by the inter-ocular distance (NRMSE) of facial landmark localization between prediction of machine learning algorithm and manually localized landmarks.