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Trial record 15 of 42 for:    Recruiting, Not yet recruiting, Available Studies | "Down Syndrome"

Digital Dysmorphology Project

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ClinicalTrials.gov Identifier: NCT02651493
Recruitment Status : Recruiting
First Posted : January 11, 2016
Last Update Posted : December 12, 2017
Sponsor:
Collaborators:
Information provided by (Responsible Party):

Study Description
Brief Summary:

In this study, the investigators propose a novel method to detect Down syndrome using photography for facial dysmorphology, a tool called computer-aided diagnosis (CAD). After validating the method, this technology will be expanded to perform similar functions to assist in the detection of other dysmorphic syndromes.

By using photography and image analysis this automated assessment tool would have the potential to improve the diagnosis rate and allow for remote, non-invasive diagnostic evaluation for dysmorphologists in a timely manner.


Condition or disease Intervention/treatment
Down Syndrome Device: photographs

Detailed Description:

In this study, investigators propose a novel method to detect Down syndrome using photography for facial dysmorphology, a tool called computer-aided diagnosis (CAD) . Local texture features based on Contourlet transform and local binary pattern are investigated to represent the facial characteristics. A support vector machine classifier is then used to discriminate between normal and abnormal cases. Accuracy, precision and recall are used to evaluate the method. After validating the method, this technology will then be expanded to perform similar functions to assist in the detection of other dysmorphic syndromes.

By using photography and image analysis this automated assessment tool would have the potential to improve the diagnosis rate and allow for remote, non-invasive diagnostic evaluation for dysmorphologists in a timely manner.


Study Design

Study Type : Interventional  (Clinical Trial)
Estimated Enrollment : 750 participants
Allocation: Non-Randomized
Intervention Model: Parallel Assignment
Masking: None (Open Label)
Primary Purpose: Health Services Research
Official Title: Down Syndrome Detection From Facial Photographs Using Machine Learning Techniques
Study Start Date : February 2013
Estimated Primary Completion Date : December 2018
Estimated Study Completion Date : December 2020

Resource links provided by the National Library of Medicine

MedlinePlus related topics: Down Syndrome
U.S. FDA Resources

Arms and Interventions

Arm Intervention/treatment
Active Comparator: Down syndrome
photographs of individuals less than 18 yo with Down syndrome
Device: photographs
computer based program to analyze photographs (computer-aided diagnosis (CAD) software)
Active Comparator: Control group
photographs of individuals less than 18 yo with a genetic referral (not Down syndrome) or a healthy sibling to a child with Down syndrome
Device: photographs
computer based program to analyze photographs (computer-aided diagnosis (CAD) software)


Outcome Measures

Primary Outcome Measures :
  1. Number of participants with Down syndrome accurately assessed by computer-aided detection (CADe) tool [ Time Frame: 5 years ]
    The study will enroll and analyze photographic data from syndromic and non-syndromic cases to investigate the parameters required to achieve an accuracy of the computer-aided detection (CADe) tool for children with genetic syndromes at a level of 90% accuracy.

  2. Number of participants with Down syndrome accurately assessed by computer-aided detection (CADe) tool [ Time Frame: 5 years ]
    The study will enroll and analyze photographic data from syndromic and non-syndromic cases to investigate the parameters required to achieve an accuracy of the computer-aided detection (CADe) tool for children with genetic syndromes at a level of 95% accuracy.


Secondary Outcome Measures :
  1. Number of participants with other dysmorphic syndromes accurately assessed by computer-aided detection (CADe) tool [ Time Frame: 5 years ]
    The study will enroll and analyze photographic data from syndromic and non-syndromic cases to investigate the parameters required to achieve an accuracy of the computer-aided detection (CADe) tool for children with genetic syndromes at a level of 90% accuracy.

  2. Number of participants with other dysmorphic syndromes accurately assessed by computer-aided detection (CADe) tool [ Time Frame: 5 years ]
    The study will enroll and analyze photographic data from syndromic and non-syndromic cases to investigate the parameters required to achieve an accuracy of the computer-aided detection (CADe) tool for children with genetic syndromes at a level of 95% accuracy.


Eligibility Criteria

Information from the National Library of Medicine

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Ages Eligible for Study:   up to 18 Years   (Child, Adult)
Sexes Eligible for Study:   All
Accepts Healthy Volunteers:   Yes
Criteria

Inclusion Criteria:

  • Pediatric subject with Down syndrome.
  • Healthy pediatric siblings of a subject with Down syndrome and/or other individuals with another genetic referral to serve as a control group.
  • Subject must be less than 18 years old.

Exclusion Criteria:

  • Subjects 18 years or older.
Contacts and Locations

Information from the National Library of Medicine

To learn more about this study, you or your doctor may contact the study research staff using the contact information provided by the sponsor.

Please refer to this study by its ClinicalTrials.gov identifier (NCT number): NCT02651493


Contacts
Contact: Sara Alyamani, BS 202 476 6099 salyaman@childrensnational.org
Contact: Marius Linguraru, PhD 202 476 3059 MLingura@childrensnational.org

Locations
United States, District of Columbia
Children's National Recruiting
Washington, District of Columbia, United States, 20010
Contact: Sara Alyamani, BS    202-476-6099    salyaman@childrensnational.org   
Contact: Kevin Cleary, PhD    202 476 3809    kcleary@childrensnational.org   
Principal Investigator: Kevin Cleary, PhD         
Sub-Investigator: Marius Linguraru, PhD         
Sponsors and Collaborators
Kevin Cleary
Sheikh Zayed Institute
George Washington University
Chiang Mai University
Investigators
Principal Investigator: Kevin Cleary, PhD Children's National
More Information

Responsible Party: Kevin Cleary, PhD, Children's Research Institute
ClinicalTrials.gov Identifier: NCT02651493     History of Changes
Other Study ID Numbers: Pro00003506.
First Posted: January 11, 2016    Key Record Dates
Last Update Posted: December 12, 2017
Last Verified: December 2017

Additional relevant MeSH terms:
Down Syndrome
Syndrome
Disease
Pathologic Processes
Intellectual Disability
Neurobehavioral Manifestations
Neurologic Manifestations
Nervous System Diseases
Abnormalities, Multiple
Congenital Abnormalities
Chromosome Disorders
Genetic Diseases, Inborn