ArticleEuropean journal of pediatrics2023
Computer-aided facial analysis as a tool to identify patients with Silver-Russell syndrome and Prader-Willi syndrome.
Article in European journal of pediatrics, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed, 9 citations in OpenAlex.
- Early identification of Prader-Willi syndrome in newborns using facial phenotype-based machine learning.Pediatric research · 2026Article
- AI-Based Facial Phenotyping Supports a Shared Molecular Axis inInternational journal of molecular sciences · 2025Article
- The impact and future of artificial intelligence in medical genetics and molecular medicine: an ongoing revolution.Functional & integrative genomics · 2024Review
- 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
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Authors and funding
6 authors at 1 institution in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Genetic syndromes often show facial features that provide clues for the diagnosis. However, memorizing these features is a challenging task for clinicians. In the last years, the app Face2Gene proved to be a helpful support for the diagnosis of genetic diseases by analyzing features detected in one or more facial images of affected individuals. Our aim was to evaluate the performance of the app in patients with Silver-Russell syndrome (SRS) and Prader-Willi syndrome (PWS). We enrolled 23 pediatric patients with clinically or genetically diagnosed SRS and 29 pediatric patients with genetically confirmed PWS. One frontal photo of each patient was acquired. Top 1, top 5, and top 10 sensitivities were analyzed. Correlation with the specific genetic diagnosis was investigated. When available, photos of the same patient at different ages were compared. In the SRS group, Face2Gene showed top 1, top 5, and top 10 sensitivities of 39%, 65%, and 91%, respectively. In 41% of patients with genetically confirmed SRS, SRS was the first syndrome suggested, while in clinically diagnosed patients, SRS was suggested as top 1 in 33% of cases (p = 0.74). Face2Gene performed better in younger patients with SRS: in all patients in whom a photo taken at a younger age than the age of enrollment was available, SRS was suggested as top 1, albeit with variable degree of probability. In the PWS group, the top 1, top 5, and top 10 sensitivities were 76%, 97%, and 100%, respectively. PWS was suggested as top 1 in 83% of patients genetically diagnosed with paternal deletion of chromosome 15q11-13 and in 60% of patients presenting with maternal uniparental disomy of chromosome 15 (p = 0.17). The performance was uniform throughout the investigated age range (1-15 years).
conclusionIn addition to a thorough medical history and detailed clinical examination, the Face2Gene app can be a useful tool to support clinicians in identifying children with a potential diagnosis of SRS or PWS. WHAT IS KNOWN: • Several genetic syndromes present typical facial features that may provide clues for the diagnosis. • Memorizing all syndromic facial characteristics is a challenging task for clinicians. WHAT IS NEW: • Face2Gene may represent a useful support for pediatricians for the diagnosis of genetic syndromes. • Face2Gene app can be a useful tool to integrate in the diagnostic path of patients with SRS and PWS.
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