ArticleInternational journal of molecular sciences2020
Evaluating Face2Gene as a Tool to Identify Cornelia de Lange Syndrome by Facial Phenotypes.
Article in International journal of molecular sciences, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 31 papers.
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Who cites it
31 citing papers in PubMed, 69 citations in OpenAlex.
- AI-Based Facial Phenotyping Supports a Shared Molecular Axis inInternational journal of molecular sciences · 2025Article
- Postzygotic mosaicism in SMC1A and the first reported case of a female with Cornelia de Lange syndrome.Scientific reports · 2025Article
- AI-assisted facial analysis in healthcare: From disease detection to comprehensive management.Patterns (New York, N.Y.) · 2025Review
- Early diagnosis of vascular Ehlers-Danlos syndrome through AI-powered facial analysis: Results from the Montalcino Aortic Consortium.Genetics in medicine open · 2025Article
- Endocrine Evaluation and Homeostatic Model Assessment in Patients with Cornelia de Lange SyndromeJournal of clinical research in pediatric endocrinology · 2024Article
- Computer-based facial recognition as an assisting diagnostic tool to identify children with Noonan syndrome.BMC pediatrics · 2024Article
- The Approach to a Child with Dysmorphic Features: What the Pediatrician Should Know.Children (Basel, Switzerland) · 2024Review
- Next generation phenotyping for diagnosis and phenotype-genotype correlations in Kabuki syndrome.Scientific reports · 2024Article
- An intragenic duplication in the AFF2 gene associated with Cornelia de Lange syndrome phenotype.Frontiers in genetics · 2024Article
- Article
- Assessing facial weakness in myasthenia gravis with facial recognition software and deep learning.Annals of clinical and translational neurology · 2023Article
- A Deep-Learning-Based Method Can Detect Both Common and Rare Genetic Disorders in Fetal Ultrasound.Biomedicines · 2023Article
- Expansion of the genotypic and phenotypic spectrum of CTCF-related disorder guides clinical management: 43 new subjects and a comprehensive literature review.American journal of medical genetics. Part A · 2023Review
- AI-based diagnosis in mandibulofacial dysostosis with microcephaly using external ear shapes.Frontiers in pediatrics · 2023Article
- Subclinical myocardial dysfunction is revealed by speckle tracking echocardiography in patients with Cornelia de Lange syndrome.The international journal of cardiovascular imaging · 2022Article
- Smartphone-Based Facial Scanning as a Viable Tool for Facially Driven Orthodontics?Sensors (Basel, Switzerland) · 2022Article
- A Novel Intragenic Duplication in theGenes · 2022Article
- Refining nosology by modelling variation among facial phenotypes: the RASopathies.Journal of medical genetics · 2022Article
- Review on Facial-Recognition-Based Applications in Disease Diagnosis.Bioengineering (Basel, Switzerland) · 2022Review
- Disease-associated c-MYC downregulation in human disorders of transcriptional regulation.Human molecular genetics · 2022Article
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Authors and funding
16 authors at 7 institutions in 4 countries.
Funding
Abstract
Characteristic or classic phenotype of Cornelia de Lange syndrome (CdLS) is associated with a recognisable facial pattern. However, the heterogeneity in causal genes and the presence of overlapping syndromes have made it increasingly difficult to diagnose only by clinical features. DeepGestalt technology, and its app Face2Gene, is having a growing impact on the diagnosis and management of genetic diseases by analysing the features of affected individuals. Here, we performed a phenotypic study on a cohort of 49 individuals harbouring causative variants in known CdLS genes in order to evaluate Face2Gene utility and sensitivity in the clinical diagnosis of CdLS. Based on the profile images of patients, a diagnosis of CdLS was within the top five predicted syndromes for 97.9% of our cases and even listed as first prediction for 83.7%. The age of patients did not seem to affect the prediction accuracy, whereas our results indicate a correlation between the clinical score and affected genes. Furthermore, each gene presents a different pattern recognition that may be used to develop new neural networks with the goal of separating different genetic subtypes in CdLS. Overall, we conclude that computer-assisted image analysis based on deep learning could support the clinical diagnosis of CdLS.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.