ArticleMolecular genetics & genomic medicine2021
Objective differential diagnosis of Noonan and Williams-Beuren syndromes in diverse populations using quantitative facial phenotyping.
Article in Molecular genetics & genomic medicine, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed, 18 citations in OpenAlex.
- Clinical and Genetic Characterization of Noonan Syndrome in a Romanian Cohort from Transylvania: Details onDiagnostics (Basel, Switzerland) · 2025Article
- Early diagnosis of vascular Ehlers-Danlos syndrome through AI-powered facial analysis: Results from the Montalcino Aortic Consortium.Genetics in medicine open · 2025Article
- GestaltMatcher Database - A global reference for facial phenotypic variability in rare human diseases.medRxiv : the preprint server for health sciences · 2024Article
- Optimization and evaluation of facial recognition models for Williams-Beuren syndrome.European journal of pediatrics · 2024Article
- GestaltMatcher Database - A global reference for facial phenotypic variability in rare human diseases.Research square · 2024Article
- Computer-based facial recognition as an assisting diagnostic tool to identify children with Noonan syndrome.BMC pediatrics · 2024Article
- Monogenic causes of familial short stature.Frontiers in endocrinology · 2024Review
- Article
- A Large Turkish Cohort of Williams Syndrome: The Evaluation of Facial, Cardiovascular, and Neuropsychiatric Features.Turkish archives of pediatrics · 2023Article
- Review on Facial-Recognition-Based Applications in Disease Diagnosis.Bioengineering (Basel, Switzerland) · 2022Review
- Automated Facial Recognition for Noonan Syndrome Using Novel Deep Convolutional Neural Network With Additive Angular Margin Loss.Frontiers in genetics · 2021Article
Corrections and comments
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Authors and funding
3 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
introductionPatients 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.
methodsWe 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.
resultsWe 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).
conclusionOur facial analysis provided for the first time quantitative reference facial metrics for the differential diagnosis Noonan and Williams-Beuren syndromes in diverse populations.
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