Objective differential diagnosis of Noonan and Williams–Beuren syndromes in diverse populations using quantitative facial phenotyping

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.