ArticleScientific reports2024
Next generation phenotyping for diagnosis and phenotype-genotype correlations in Kabuki syndrome.
Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers, 1 of them a synthesis that pooled it.
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Who cites it
13 citing papers in PubMed, 1 synthesis or guideline pooled it, 16 citations in OpenAlex.
- Explainable Artificial Intelligence in Dentistry: A Systematic Review of Its Trust and Translation.International dental journal · 2026Pooled it
- Diagnosis of rare diseases based on facial phenotype: a quantitative assessment using 2D and 3D photography in Stickler syndrome.Orphanet journal of rare diseases · 2026Article
- Evaluating Retrieval-Augmented Generation-Large Language Models for Infective Endocarditis Prophylaxis: Clinical Accuracy and Efficiency.International dental journal · 2026Article
- Histone acetylation and methylation in rare diseases: from molecular mechanisms to clinical presentations.Frontiers in cell and developmental biology · 2026Review
- AI-Based Facial Phenotyping Supports a Shared Molecular Axis inInternational journal of molecular sciences · 2025Article
- Applying artificial intelligence to rare diseases: a literature review highlighting lessons from Fabry disease.Orphanet journal of rare diseases · 2025Review
- Clinical delineation and genotype-phenotype correlation in 104 children with kabuki syndrome: A single-center, cross-sectional and follow-up study in China.European journal of pediatrics · 2025Article
- Reply to Comment on Letter to the Editor: Humanitarian Facial Recognition for Rare Craniofacial Malformations.Plastic and reconstructive surgery. Global open · 2025Article
- Article
- 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
- Humanitarian Facial Recognition for Rare Craniofacial Malformations.Plastic and reconstructive surgery. Global open · 2024Article
- Automated fingerprint analysis as a diagnostic tool for the genetic disorder Kabuki syndrome.Genetics in medicine open · 2024Article
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Authors and funding
25 authors at 6 institutions in 3 countries.
Funding
Abstract
The field of dysmorphology has been changed by the use Artificial Intelligence (AI) and the development of Next Generation Phenotyping (NGP). The aim of this study was to propose a new NGP model for predicting KS (Kabuki Syndrome) on 2D facial photographs and distinguish KS1 (KS type 1, KMT2D-related) from KS2 (KS type 2, KDM6A-related). We included retrospectively and prospectively, from 1998 to 2023, all frontal and lateral pictures of patients with a molecular confirmation of KS. After automatic preprocessing, we extracted geometric and textural features. After incorporation of age, gender, and ethnicity, we used XGboost (eXtreme Gradient Boosting), a supervised machine learning classifier. The model was tested on an independent validation set. Finally, we compared the performances of our model with DeepGestalt (Face2Gene). The study included 1448 frontal and lateral facial photographs from 6 centers, corresponding to 634 patients (527 controls, 107 KS); 82 (78%) of KS patients had a variation in the KMT2D gene (KS1) and 23 (22%) in the KDM6A gene (KS2). We were able to distinguish KS from controls in the independent validation group with an accuracy of 95.8% (78.9-99.9%, p < 0.001) and distinguish KS1 from KS2 with an empirical Area Under the Curve (AUC) of 0.805 (0.729-0.880, p < 0.001). We report an automatic detection model for KS with high performances (AUC 0.993 and accuracy 95.8%). We were able to distinguish patients with KS1 from KS2, with an AUC of 0.805. These results outperform the current commercial AI-based solutions and expert clinicians.
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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.