Digital Dysmorphology Project
| Status: | Recruiting |
|---|---|
| Conditions: | Other Indications |
| Therapuetic Areas: | Other |
| Healthy: | No |
| Age Range: | Any - 18 |
| Updated: | 1/6/2019 |
| Start Date: | February 2013 |
| End Date: | December 2023 |
| Contact: | Sara Alyamani, BS |
| Email: | salyaman@childrensnational.org |
| Phone: | 202 476 6099 |
Down Syndrome Detection From Facial Photographs Using Machine Learning Techniques
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.
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.
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.
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.
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.
We found this trial at
1
site
Washington, District of Columbia 20010
Principal Investigator: Kevin Cleary, PhD
Phone: 202-476-6099
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