Down Syndrome
Conditions
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.
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.
Interventions
DEVICEphotographs
computer based program to analyze photographs (computer-aided diagnosis (CAD) software)
Sponsors
Children's National Research Institute
George Washington University
Chiang Mai University
Kevin Cleary
Study design
Allocation
NON_RANDOMIZED
Intervention model
PARALLEL
Primary purpose
HEALTH_SERVICES_RESEARCH
Masking
NONE
Eligibility
Sex/Gender
ALL
Age
No minimum to 18 Years
Healthy volunteers
Yes
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.
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| Number of participants with Down syndrome accurately assessed by computer-aided detection (CADe) tool | 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. |
Secondary
| Measure | Time frame | Description |
|---|---|---|
| Number of participants with other dysmorphic syndromes accurately assessed by computer-aided detection (CADe) tool | 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. |
Countries
United States
Contacts
Primary ContactKevin Cleary, PhD
Backup ContactMarius Linguraru, PhD
Outcome results
None listed