aidy.eu
AIDY
Artificial intelligence for rare-disease diagnosis. From a simple front and profile photo, the AIDY app directs patients with rare diseases toward the right care pathway, with a geolocated referral.
Visit aidy.eu →The problem
5 years of diagnostic wandering
According to the WHO, there are between 6,000 and 8,000 different rare diseases, affecting about 300 million people worldwide — about 3 million in France. 80% are genetic in origin and often affect children. On average, patients wait 5 years and see 8 doctors before getting a diagnosis.
750+
genetic syndromes detectable with distinctive facial features
90%
average detection accuracy
50K+
face images analyzed in the database
How it works
Photo analysis in seconds
Secure sign-in via the e-CPS portal or by invitation. The user photographs the face from the front and in profile in good lighting; the algorithm compares facial traits against AIDY's database and suggests a list of likely syndromes ranked by probability, with a geolocated referral to expert centers.
GDPR-compliant, hosted on HDS-certified (Hébergeur de Données de Santé) French servers; photos are not stored on AIDY's servers, only on the user's phone.
Medical device pending certification — does not replace a medical diagnosis.
Research
Synthetic faces and privacy
Teaching and diagnosing rare diseases relies on observing characteristic facial traits — but sharing these images creates a dilemma between transmitting knowledge and protecting patients. AIDY is developing a synthetic-face-generation method: from a small number of real diagnosed-patient photos, an AI learns the disease's characteristic traits and then generates hundreds of synthetic face variations — faithfully reproducing the phenotypic traits without reproducing any real patient's identity.
Team
Who's behind AIDY
Antoine Ferry
President of the association
Roman Hossein Khonsari
Surgeon, researcher at Institut Imagine
Nicolas Garcelon
Research Director, Institut Imagine
Olivier Lienhard
CTO
Quentin Hennocq
Researcher
Thomas Bongibault
Researcher
Ahmed Zaiter
Researcher
Publications
Research and innovation
Evaluation of a New Inclusive Next-Generation Synthetic Face Tool for Dysmorphology.
Next Generation Phenotyping and Synthetic Faces in Coffin Siris Syndrome.
Humanitarian Facial Recognition for Rare Craniofacial Malformations.
Artificial intelligence-based diagnosis in fetal pathology using external ear shapes.
AI-based diagnosis and phenotype–genotype correlations in syndromic craniosynostoses.
Next generation phenotyping for diagnosis and phenotype–genotype correlations in Kabuki syndrome.
AI-based diagnosis in mandibulofacial dysostosis with microcephaly using external ear shapes.
An automatic facial landmarking for children with rare diseases.
