Introduction: Patients with Noonan and Williams-Beuren syndrome present similar facial phenotypes modulated by their ethnic background. Although distinctive facial features have been reported, studies show a variable incidence of those characteristics in populations with diverse ancestry. Hence, a differential diagnosis based on reported facial features can be challenging. Although accurate diagnoses are possible with genetic testing, they are not available in developing and remote regions.
Methods: We used a facial analysis technology to identify the most discriminative facial metrics between 286 patients with Noonan and 161 with Williams-Beuren syndrome with diverse ethnic background. We quantified the most discriminative metrics, and their ranges both globally and in different ethnic groups. We also created population-based appearance images that are useful not only as clinical references but also for training purposes. Finally, we trained both global and ethnic-specific machine learning models with previous metrics to distinguish between patients with Noonan and Williams-Beuren syndromes.
Results: We obtained a classification accuracy of 85.68% in the global population evaluated using cross-validation, which improved to 90.38% when we adapted the facial metrics to the ethnicity of the patients (p = 0.024).
Conclusion: Our facial analysis provided for the first time quantitative reference facial metrics for the differential diagnosis Noonan and Williams-Beuren syndromes in diverse populations.
Keywords: Noonan; Williams-Beuren; facial analysis; facial phenotyping; machine learning.
© 2021 The Authors. Molecular Genetics & Genomic Medicine published by Wiley Periodicals LLC.