Skip to main content
bioRxiv
  • Home
  • About
  • Submit
  • ALERTS / RSS
Advanced Search
New Results

Advances in computer-assisted syndrome recognition and differentiation in a set of metabolic disorders

Jean Tori Pantel, Max Zhao, Martin Atta Mensah, Nurulhuda Hajjir, Tzung-Chien Hsieh, Yair Hanani, Nicole Fleischer, Tom Kamphans, Stefan Mundlos, Yaron Gurovich, Peter M. Krawitz
doi: https://doi.org/10.1101/219394
Jean Tori Pantel
1Institute for Medical Genetics and Human Genetics, Charité University Medicine, Berlin, Germany
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Max Zhao
1Institute for Medical Genetics and Human Genetics, Charité University Medicine, Berlin, Germany
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Martin Atta Mensah
1Institute for Medical Genetics and Human Genetics, Charité University Medicine, Berlin, Germany
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Nurulhuda Hajjir
1Institute for Medical Genetics and Human Genetics, Charité University Medicine, Berlin, Germany
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Tzung-Chien Hsieh
1Institute for Medical Genetics and Human Genetics, Charité University Medicine, Berlin, Germany
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Yair Hanani
2FDNA, Boston, USA GeneTalk, Bonn, Germany
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Nicole Fleischer
2FDNA, Boston, USA GeneTalk, Bonn, Germany
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Tom Kamphans
3Institute for Genomic Statistics and Bioinformatics, Rheinische Friedrich-Wilhelms-Universität Bonn, Germany
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Stefan Mundlos
1Institute for Medical Genetics and Human Genetics, Charité University Medicine, Berlin, Germany
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Yaron Gurovich
2FDNA, Boston, USA GeneTalk, Bonn, Germany
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Peter M. Krawitz
1Institute for Medical Genetics and Human Genetics, Charité University Medicine, Berlin, Germany
3Institute for Genomic Statistics and Bioinformatics, Rheinische Friedrich-Wilhelms-Universität Bonn, Germany
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • Abstract
  • Full Text
  • Info/History
  • Metrics
  • Supplementary material
  • Preview PDF
Loading

Abstract

Significant improvements in automated image analysis have been achieved over the recent years and tools are now increasingly being used in computer-assisted syndromology. However, the recognizability of the 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 a metabolic disease and Nicolaides-Baraitser syndrome as another disorder that is also characterized by coarse facies. A classifier that was trained on these five cohorts, comprising 288 patients in total, achieved a mean accuracy of 62%.

The performance of automated image analysis is not only significantly higher than randomly expected but also better than in previous approaches. In part this might be explained by our large training sets. We therefore set up a simulation pipeline that is suited to analyze the effect of different potential confounders, such as cohort size, age, sex, or ethnic background on the recognizability 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 recognizability is a phenotype-specific value, that hasn’t 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.

Availability: software for classification: https://app.face2gene.com/research,

Abbreviations
DDx
Differential Diagnoses
DPDL
Deep Phenotyping for Deep Learning
DS
Down syndrome
FDNA
Facial Dysmorphology Novel Analysis
FNR
False Negative Rate
FPR
False Positive Rate
GAG
Glycosaminoglycan
HPO
Human Phenotype Ontology
LSD
Lysosomal Storage Disease
ML
Mucolipidosis
MPS I
Mucopolysaccharidosis type I
MPS II
Mucopolysaccharidosis type II
NCBRS
Nicolaides-Baraitser Syndrome
ROC
Receiver Operating Characteristics
SLOS
Smith-Lemli-Opitz Syndrome
TPR
True Positive Rate
  • Abbreviations
    DDx
    Differential Diagnoses
    DPDL
    Deep Phenotyping for Deep Learning
    DS
    Down syndrome
    FDNA
    Facial Dysmorphology Novel Analysis
    FNR
    False Negative Rate
    FPR
    False Positive Rate
    GAG
    Glycosaminoglycan
    HPO
    Human Phenotype Ontology
    LSD
    Lysosomal Storage Disease
    ML
    Mucolipidosis
    MPS I
    Mucopolysaccharidosis type I
    MPS II
    Mucopolysaccharidosis type II
    NCBRS
    Nicolaides-Baraitser Syndrome
    ROC
    Receiver Operating Characteristics
    SLOS
    Smith-Lemli-Opitz Syndrome
    TPR
    True Positive Rate
  • Copyright 
    The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license.
    Back to top
    PreviousNext
    Posted November 14, 2017.
    Download PDF

    Supplementary Material

    Email

    Thank you for your interest in spreading the word about bioRxiv.

    NOTE: Your email address is requested solely to identify you as the sender of this article.

    Enter multiple addresses on separate lines or separate them with commas.
    Advances in computer-assisted syndrome recognition and differentiation in a set of metabolic disorders
    (Your Name) has forwarded a page to you from bioRxiv
    (Your Name) thought you would like to see this page from the bioRxiv website.
    CAPTCHA
    This question is for testing whether or not you are a human visitor and to prevent automated spam submissions.
    Share
    Advances in computer-assisted syndrome recognition and differentiation in a set of metabolic disorders
    Jean Tori Pantel, Max Zhao, Martin Atta Mensah, Nurulhuda Hajjir, Tzung-Chien Hsieh, Yair Hanani, Nicole Fleischer, Tom Kamphans, Stefan Mundlos, Yaron Gurovich, Peter M. Krawitz
    bioRxiv 219394; doi: https://doi.org/10.1101/219394
    Reddit logo Twitter logo Facebook logo LinkedIn logo Mendeley logo
    Citation Tools
    Advances in computer-assisted syndrome recognition and differentiation in a set of metabolic disorders
    Jean Tori Pantel, Max Zhao, Martin Atta Mensah, Nurulhuda Hajjir, Tzung-Chien Hsieh, Yair Hanani, Nicole Fleischer, Tom Kamphans, Stefan Mundlos, Yaron Gurovich, Peter M. Krawitz
    bioRxiv 219394; doi: https://doi.org/10.1101/219394

    Citation Manager Formats

    • BibTeX
    • Bookends
    • EasyBib
    • EndNote (tagged)
    • EndNote 8 (xml)
    • Medlars
    • Mendeley
    • Papers
    • RefWorks Tagged
    • Ref Manager
    • RIS
    • Zotero
    • Tweet Widget
    • Facebook Like
    • Google Plus One

    Subject Area

    • Genetics
    Subject Areas
    All Articles
    • Animal Behavior and Cognition (4399)
    • Biochemistry (9637)
    • Bioengineering (7127)
    • Bioinformatics (24959)
    • Biophysics (12677)
    • Cancer Biology (10002)
    • Cell Biology (14406)
    • Clinical Trials (138)
    • Developmental Biology (7992)
    • Ecology (12154)
    • Epidemiology (2067)
    • Evolutionary Biology (16030)
    • Genetics (10957)
    • Genomics (14784)
    • Immunology (9911)
    • Microbiology (23750)
    • Molecular Biology (9516)
    • Neuroscience (51103)
    • Paleontology (370)
    • Pathology (1546)
    • Pharmacology and Toxicology (2694)
    • Physiology (4038)
    • Plant Biology (8700)
    • Scientific Communication and Education (1512)
    • Synthetic Biology (2406)
    • Systems Biology (6461)
    • Zoology (1350)