Digital Dysmorphology Project

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.

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.

photographs of individuals less than 18 yo with a genetic referral (not Down syndrome) or a healthy sibling to a child with Down syndrome

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.

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.

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.

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.

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.