Enhanced Patient Evaluation with Next-Generation Phenotyping
Clinicians globally use Face2Gene CLINIC to enhance patient evaluations with deep phenotyping technology. The CLINIC application uses advanced facial analysis and deep learning technologies to assist in detecting syndrome-related phenotypes and disease-causing genes correlated with rare diseases during clinical exams.
Collaborative Case Review for Diagnostic Dilemmas
Face2Gene FORUMS acts as an extension of CLINIC—providing a place to collaborate with colleagues on undiagnosed cases. Clinicians work together, either with their own team or with the acclaimed Expert Review Panel in the Face2Gene Unknown Forum, to give and receive clinical feedback on difficult to diagnose cases.
The ever-growing database of syndromes, phenotypes and genes in Face2Gene LIBRARY acts as a reference resource for medical professionals worldwide. With curation from the genetics community and exclusive access to the London Medical Databases (LMD), LIBRARY remains a top resource for up-to-date content on rare and genetic diseases.
Genetic Variant Prioritization Supported by Next-Generation Phenotyping
Face2Gene LABS enhances the interpretation of genomic test results by enabling clinicians to send detailed patient phenotype information, medical annotations and deep learning insights directly into the molecular interpretation pipeline, highlighting clinically-relevant genetic variants to consider.
Accelerating Clinical Genomic Discoveries
Clinical genomic studies are accelerated using facial analysis and artificial intelligence. Face2Gene RESEARCH yields greater genomic insights by enabling users to use this technology in collaborative studies while leveraging data from a growing research community.
Patterns of Morphology Training on any Device
Learning to recognize phenotypic features and rare diseases is made simple with Face2Gene ACADEMY. An interactive training tool which can be used on any device, ACADEMY helps students and experts alike to better recognize patterns of morphology that have associations with disease.