SynthesisJournal of inherited metabolic disease2018
Advances in computer-assisted syndrome recognition by the example of inborn errors of metabolism.
Synthesis in Journal of inherited metabolic disease, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 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
17 citing papers in PubMed, 46 citations in OpenAlex.
- Utility of Face2Gene's DeepGestalt and D-Score applications in paediatric neurodevelopmental disorders in South Africa.European journal of human genetics : EJHG · 2026Article
- Unlocking 3D baby face photogrammetry: Multi-view BabyMorph reconstruction from uncalibrated photographs.Expert systems with applications · 2026Article
- Loss-of-Function Variants in CUL3 Cause a Syndromic Neurodevelopmental Disorder.Annals of neurology · 2024Article
- Validation of 3 Computer-Aided Facial Phenotyping Tools (DeepGestalt, GestaltMatcher, and D-Score): Comparative Diagnostic Accuracy Study.Journal of medical Internet research · 2024Article
- 3D facial analysis for rare disease diagnosis and treatment monitoring: Proof-Of-Concept plan for hereditary angioedema.PLOS digital health · 2023Article
- Review on Facial-Recognition-Based Applications in Disease Diagnosis.Bioengineering (Basel, Switzerland) · 2022Review
- 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
- Efficiency of Computer-Aided Facial Phenotyping (DeepGestalt) in Individuals With and Without a Genetic Syndrome: Diagnostic Accuracy Study.Journal of medical Internet research · 2020Article
- Differentiating molecular etiologies of Angelman syndrome through facial phenotyping using deep learning.American journal of medical genetics. Part A · 2020Article
- Evaluating Face2Gene as a Tool to Identify Cornelia de Lange Syndrome by Facial Phenotypes.International journal of molecular sciences · 2020Article
- Success of Face Analysis Technology in Rare Genetic Diseases Diagnosed by Whole-Exome Sequencing: A Single-Center Experience.Molecular syndromology · 2020Article
- Differentiation of MISSLA and Fanconi anaemia by computer-aided image analysis and presentation of two novel MISSLA siblings.European journal of human genetics : EJHG · 2019Article
- PEDIA: prioritization of exome data by image analysis.Genetics in medicine : official journal of the American College of Medical Genetics · 2019Article
- Mutations in PIGU Impair the Function of the GPI Transamidase Complex, Causing Severe Intellectual Disability, Epilepsy, and Brain Anomalies.American journal of human genetics · 2019Article
- The utility of phenomics in diagnosis of inherited metabolic disorders.Clinical medicine (London, England) · 2019Review
- Enabling Global Clinical Collaborations on Identifiable Patient Data: The Minerva Initiative.Frontiers in genetics · 2019Article
- Characterization of glycosylphosphatidylinositol biosynthesis defects by clinical features, flow cytometry, and automated image analysis.Genome medicine · 2018Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
11 authors at 3 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Significant improvements in automated image analysis have been achieved in recent years and tools are now increasingly being used in computer-assisted syndromology. However, the ability to recognize a syndromic facial gestalt might depend on the syndrome and may also be confounded by severity of phenotype, size of available training sets, ethnicity, age, and sex. Therefore, benchmarking and comparing the performance of deep-learned classification processes is inherently difficult. For a systematic analysis of these influencing factors we chose the lysosomal storage diseases mucolipidosis as well as mucopolysaccharidosis type I and II that are known for their wide and overlapping phenotypic spectra. For a dysmorphic comparison we used Smith-Lemli-Opitz syndrome as another inborn error of metabolism and Nicolaides-Baraitser syndrome as another disorder that is also characterized by coarse facies. A classifier that was trained on these five cohorts, comprising 289 patients in total, achieved a mean accuracy of 62%. We also developed a simulation framework to analyze the effect of potential confounders, such as cohort size, age, sex, or ethnic background on the distinguishability of phenotypes. We found that the true positive rate increases for all analyzed disorders for growing cohorts (n = [10...40]) while ethnicity and sex have no significant influence. The dynamics of the accuracies strongly suggest that the maximum distinguishability is a phenotype-specific value, which has not been reached yet for any of the studied disorders. This should also be a motivation to further intensify data sharing efforts, as computer-assisted syndrome classification can still be improved by enlarging the available training sets.
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.