Ignite Creation Date:
2025-12-25 @ 1:22 AM
Ignite Modification Date:
2026-02-20 @ 6:01 PM
Study NCT ID:
NCT02651493
Status:
UNKNOWN
Last Update Posted:
2023-02-01
First Post:
2015-10-22
Is NOT Gene Therapy:
True
Has Adverse Events:
False
Brief Title:
Digital Dysmorphology Project
Sponsor:
Kevin Cleary
Organization:
Study Overview
Official Title:
Down Syndrome Detection From Facial Photographs Using Machine Learning Techniques
Status:
UNKNOWN
Status Verified Date:
2023-01
Last Known Status:
RECRUITING
Delayed Posting:
No
If Stopped, Why?:
Not Stopped
Has Expanded Access:
False
If Expanded Access, NCT#:
N/A
Has Expanded Access, NCT# Status:
N/A
Acronym:
None
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.
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.
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 Oversight
Has Oversight DMC:
False
Is a FDA Regulated Drug?:
None
Is a FDA Regulated Device?:
None
Is an Unapproved Device?:
None
Is a PPSD?:
None
Is a US Export?:
None
Is an FDA AA801 Violation?: