ArticleMolecular syndromology2020
Success of Face Analysis Technology in Rare Genetic Diseases Diagnosed by Whole-Exome Sequencing: A Single-Center Experience.
Article in Molecular syndromology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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15 citing papers in PubMed, 27 citations in OpenAlex.
- Machine Learning, Large Language Models, and Multimodal AI for Diagnosing Pediatric Rare Diseases: Scoping Review.Journal of medical Internet research · 2026Article
- Utility of Face2Gene's DeepGestalt and D-Score applications in paediatric neurodevelopmental disorders in South Africa.European journal of human genetics : EJHG · 2026Article
- Evaluation of a Facial Dysmorphology Analysis Algorithm (Face2Gene) in Identifying Treacher Collins Syndrome Amongst Diverse Population.Orthodontics & craniofacial research · 2026Article
- The Utility of Face2Gene App for Syndrome Recognition in Indian Children with Dysmorphism.Indian journal of pediatrics · 2026Article
- GestaltMatcher Database - A global reference for facial phenotypic variability in rare human diseases.medRxiv : the preprint server for health sciences · 2024Article
- GestaltMatcher Database - A global reference for facial phenotypic variability in rare human diseases.Research square · 2024Article
- Validation of 3 Computer-Aided Facial Phenotyping Tools (DeepGestalt, GestaltMatcher, and D-Score): Comparative Diagnostic Accuracy Study.Journal of medical Internet research · 2024Article
- First Italian experience using the automated craniofacial gestalt analysis on a cohort of pediatric patients with multiple anomaly syndromes.Italian journal of pediatrics · 2022Article
- Case Report: The success of face analysis technology in extremely rare genetic diseases in Korea: Tatton-Brown-Rahman syndrome and Say-Barber -Biesecker-Young-Simpson variant of ohdo syndrome.Frontiers in genetics · 2022Article
- Deep learning-based facial image analysis in medical research: a systematic review protocol.BMJ open · 2021Article
- Facial Recognition Intensity in Disease Diagnosis Using Automatic Facial Recognition.Journal of personalized medicine · 2021Article
- Williams syndrome.Nature reviews. Disease primers · 2021Review
- The History of Gene Hunting in Hereditary Spinocerebellar Degeneration: Lessons From the Past and Future Perspectives.Frontiers in genetics · 2021Review
- Variants of significance: medical genetics and surgical outcomes in congenital heart disease.Current opinion in pediatrics · 2020Review
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2 authors at 2 institutions in 1 country.
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No grant is acknowledged in the PubMed record.
Abstract
The diagnosis of rare genetic diseases is one of the most difficult areas in medicine. Whole-exome sequencing (WES) technology makes it easier to diagnose these diseases. In addition, next-generation phenotyping can help to diagnose computer-based algorithms. Detailed dysmorphologic findings of 25 patients diagnosed by WES in our center were described. The success of this technology in diagnosing rare genetic diseases was investigated by scanning the photographs of 25 patients with Face2Gene application. The application listed possible preliminary diagnoses (30 disease suggestion). Of these, 12 (48%) cases were correctly matched. The most common disease group in the patients was neurological disease (96%). The most common mode of inheritance in the patients was autosomal recessive. The rate of consanguineous marriages was determined in 80% of the patients. Ten patients had microcephaly and 7 patients had corpus callosum anomaly. In our study, we found that the success of Face2Gene was lower than described in the literature. We think that the probable cause of this condition is that the cases are very rare, and there is not enough data about these diseases in the application. Therefore, it is recommended that applications should be used more frequently by pediatricians and clinical geneticists. The diagnosis of rare diseases still is quite difficult. Nowadays, WES is a successful method. However, applications such as Face2Gene help to make a clinical prediagnosis and create a larger database.
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