Digital Dysmorphology Project
Purpose
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
- Down Syndrome
Eligibility
- Eligible Ages
- Under 18 Years
- Eligible Genders
- All
- Accepts 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.
Study Design
- Phase
- N/A
- Study Type
- Interventional
- Allocation
- Non-Randomized
- Intervention Model
- Parallel Assignment
- Primary Purpose
- Health Services Research
- Masking
- None (Open Label)
Arm Groups
| Arm | Description | Assigned Intervention |
|---|---|---|
|
Active Comparator Down syndrome |
photographs of individuals less than 18 yo with Down syndrome |
|
|
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 |
|
Recruiting Locations
Children's National and nearby locations
Washington, District of Columbia 20010
More Details
- NCT ID
- NCT02651493
- Status
- Recruiting
- Sponsor
- Kevin Cleary
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.