Digital Dysmorphology Project

Down Syndrome Clinical Trial

Official title:

Down Syndrome Detection From Facial Photographs Using Machine Learning Techniques

Clinical Trial Details — Status: Recruiting

Administrative data

• NCT number: NCT02651493
• Other study ID #: Pro00003506.
• Status: Recruiting
• Phase: N/A
• Start date: February 2013
• Est. completion date: December 2023

Study information

• Verified date: January 2023
• Source: Children's National Research Institute
• Contact: Kevin Cleary, PhD
• Phone: 202 476 3809
• Email: kcleary[@]childrensnational.org
• Is FDA regulated: No
• Study type: Interventional

Clinical Trial 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.

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.

Recruitment information / eligibility

• Status: Recruiting
• Enrollment: 750
• Est. completion date: December 2023
• Est. primary completion date: December 2023
• Accepts healthy volunteers: Accepts Healthy Volunteers
• Gender: All
• Age group: N/A to 18 Years

Eligibility

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.

Related Conditions & MeSH terms

• Down Syndrome
• Syndrome

Intervention

Device:
• photographs
computer based program to analyze photographs (computer-aided diagnosis (CAD) software)

Locations

Country	Name	City	State
United States	Children's National	Washington	District of Columbia

Sponsors (4)

Lead Sponsor	Collaborator
Kevin Cleary	Chiang Mai University, Children's National Research Institute, George Washington University

Country where clinical trial is conducted

United States, 

Outcome

Type	Measure	Description	Time frame	Safety issue
Primary	Number of participants with Down syndrome accurately assessed by computer-aided detection (CADe) tool	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.	5 years
Primary	Number of participants with Down syndrome accurately assessed by computer-aided detection (CADe) tool	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.	5 years
Secondary	Number of participants with other dysmorphic syndromes accurately assessed by computer-aided detection (CADe) tool	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.	5 years
Secondary	Number of participants with other dysmorphic syndromes accurately assessed by computer-aided detection (CADe) tool	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.	5 years