ArticleGenetics in medicine : official journal of the American College of Medical Genetics2019
PEDIA: prioritization of exome data by image analysis.
Article in Genetics in medicine : official journal of the American College of Medical Genetics, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 37 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
37 citing papers in PubMed.
- Leveraging Next-Generation Phenotyping in Dysmorphology to Support Variant Interpretation in Mowat-Wilson Syndrome.Neurology. Genetics · 2026Article
- AI-Driven Multidimensional Phenotyping for Variant Classification in Rare Neurogenetic Disorders.Neurology. Genetics · 2026Article
- Perspectives on the Current and Future State of Artificial Intelligence in Medical Genetics.American journal of medical genetics. Part A · 2025Review
- Workflow analysis and evaluation of a next-generation phenotyping tool: A qualitative study of Face2Gene.European journal of human genetics : EJHG · 2025Article
- Review
- Few shot learning for phenotype-driven diagnosis of patients with rare genetic diseases.NPJ digital medicine · 2025Article
- Artificial intelligence-driven genotype-epigenotype-phenotype approaches to resolve challenges in syndrome diagnostics.EBioMedicine · 2025Article
- Artificial intelligence in clinical genetics.European journal of human genetics : EJHG · 2025Review
- GestaltGAN: synthetic photorealistic portraits of individuals with rare genetic disorders.European journal of human genetics : EJHG · 2025Article
- A 3D Clinical Face Phenotype Space of Genetic Syndromes Using a Triplet-Based Singular Geometric Autoencoder.IEEE access : practical innovations, open solutions · 2025Article
- Acromesomelic Dysplasia With Homozygosity for a Likely Pathogenic BMPR1B Variant: Postaxial Polydactyly as a Novel Clinical Finding.Molecular genetics & genomic medicine · 2024Article
- Next-generation phenotyping integrated in a national framework for patients with ultrarare disorders improves genetic diagnostics and yields new molecular findings.Nature genetics · 2024Article
- Article
- Enhancing Variant Prioritization in VarFish through On-Premise Computational Facial Analysis.Genes · 2024Article
- Validation of 3 Computer-Aided Facial Phenotyping Tools (DeepGestalt, GestaltMatcher, and D-Score): Comparative Diagnostic Accuracy Study.Journal of medical Internet research · 2024Article
- Molecular mechanisms of human overgrowth and use ofFrontiers in genetics · 2024Review
- [Next-generation phenotyping in rare diseases with facial dysmorphism].Innere Medizin (Heidelberg, Germany) · 2023Review
- PhenoScore quantifies phenotypic variation for rare genetic diseases by combining facial analysis with other clinical features using a machine-learning framework.Nature genetics · 2023Article
- The future role of facial image analysis in ACMG classification guidelines.Medizinische Genetik : Mitteilungsblatt des Berufsverbandes Medizinische Genetik e.V · 2023Article
- Carney complex- why thorough medical history taking is so important - report of three cases and review of the literature.Endocrine · 2023Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
94 authors.
Funding
Abstract
purposePhenotype information is crucial for the interpretation of genomic variants. So far it has only been accessible for bioinformatics workflows after encoding into clinical terms by expert dysmorphologists.
methodsHere, we introduce an approach driven by artificial intelligence that uses portrait photographs for the interpretation of clinical exome data. We measured the value added by computer-assisted image analysis to the diagnostic yield on a cohort consisting of 679 individuals with 105 different monogenic disorders. For each case in the cohort we compiled frontal photos, clinical features, and the disease-causing variants, and simulated multiple exomes of different ethnic backgrounds.
resultsThe additional use of similarity scores from computer-assisted analysis of frontal photos improved the top 1 accuracy rate by more than 20-89% and the top 10 accuracy rate by more than 5-99% for the disease-causing gene.
conclusionImage analysis by deep-learning algorithms can be used to quantify the phenotypic similarity (PP4 criterion of the American College of Medical Genetics and Genomics guidelines) and to advance the performance of bioinformatics pipelines for exome analysis.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
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.