Dysmorphia, Dysmorphies Craniofaciales, Orphan Diseases
Conditions
Keywords
artificial intelligence
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
Interventions
OTHERClinical data reuse
Clinical data reuse
Sponsors
Imagine Institute
Study design
Observational model
COHORT
Time perspective
RETROSPECTIVE
Eligibility
Sex/Gender
ALL
Healthy volunteers
No
Inclusion criteria
The patient inclusion criteria are: * 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.
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| Learning an algorithm on 2D front and profile photographs, by extracting geometric and textural features, to help the practitioner carry out a diagnosis. | 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
| Measure | Time frame | Description |
|---|---|---|
| Carry out phenotype/genotype correlations to explain the phenotype of a particular genetic variant | through study completion, an average of 1 year | Carry out phenotype/genotype correlations to explain the phenotype of a particular genetic variant |
| Study the facial characteristics of a syndrome depending on ethnicity | through study completion, an average of 1 year | Study the facial characteristics of a syndrome depending on ethnicity |
| 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 age |
Countries
France
Contacts
Primary ContactYasmine Ainouz, MD
Outcome results
None listed