Automatic Phenotyping of Patients on 2D Photography
Trial Snapshot
- Phase
- Not Applicable
- Status
- Recruiting
- Sponsor
- Enrollment
- 22,000
- Locations
- 1
- Primary Endpoint
- Learning an algorithm on 2D front and profile photographs, by extracting geometric and textural features, to help the practitioner carry out a diagnosis.
Study Overview
Brief Summary
The field of artificial intelligence is booming in medicine and in the field of diagnosis. The data can be varied: x-rays, pathology sections, or photographs.
It is considered that 30 to 40% of the 7000 rare diseases described to date cause craniofacial dysmorphia. Their detection sometimes requires the trained eye of a geneticist, because certain phenotypic traits are subtle. These diagnostic difficulties and the fact that certain diseases are extremely uncommon lead to considerable diagnostic delays
Study Design
- Study Type
- Observational
- Observational Model
- Cohort
- Time Perspective
- Retrospective
Eligibility Criteria
- Sex
- All
- Accepts Healthy Volunteers
- No
Inclusion Criteria
- •Patients followed in medical genetics,
- •Patients undergoing maxillofacial surgery, or craniofacial surgery as part of the management of a pathology, of genetic origin or not, associated with dysmorphism of the head and neck,
- •Patients for whom frontal and profile facial photographs are taken as part of their treatment.
- •The inclusion criteria for control subjects are:
- •Patients followed in maxillofacial surgery, for a disease other than a rare disease associated with dysmorphia in the head or neck: acute pathology (wound) or chronic (gynecomastia).
- •Patients for whom frontal and profile facial photographs are taken as part of their treatment.
- •The criteria for non-inclusion of patients are:
- •Patients who have undergone facial or skull surgery before the first photo was taken.
- •Person subject to a judicial safeguard measure.
- •People objecting to the reuse of their health data.
- •The criteria for non-inclusion of control subjects are:
- •Pathologies affecting facial symmetry (dental cellulitis, displaced fractures).
- •Patient followed for dysmorphic syndrome or in whom dysmorphic syndrome has been suspected.
- •Person subject to a judicial safeguard measure.
- •People objecting to the reuse of their health data.
Exclusion Criteria
- Not provided
Outcomes
Primary Outcomes
Learning an algorithm on 2D front and profile photographs, by extracting geometric and textural features, to help the practitioner carry out a diagnosis.
Time Frame: through study completion, an average of 1 year
Learning an algorithm on 2D front and profile photographs, by extracting geometric and textural features, to help the practitioner carry out a diagnosis.
Secondary Outcomes
- Carry out phenotype/genotype correlations to explain the phenotype of a particular genetic variant(through study completion, an average of 1 year)
- Study the facial characteristics of a syndrome depending on age(through study completion, an average of 1 year)
- Study the facial characteristics of a syndrome depending on ethnicity(through study completion, an average of 1 year)