Ongoing Projects
Photorealistic Synthetic Portraits
Here we show photorealistic synthetic portraits of certain rare diseases based on the cohort ... Read more
Alagille Syndrome Project
Alagille syndrome is a rare genetic disorder that can affect multiple organs, including the liver, heart, and... Read more
GMDB aims to improve the openness and accessibility of scientific findings and to enhance collaboration amongst researchers and clinicians. On this page, you can find all 10 active research projects approved since 2022 until today.
To evaluate and improve how features from GesaltMatcher can serve as additional modalities to facilitate genetic diagnosis of patients with HPO information or clinical texts
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Principal Investigator: Prof. Kai Wang
Institution: Children's Hospital of Philadelphia
We will use the GMDB database and related software tools such as GestaltMatcher to characterize features of suspected genetic diseases and to evaluate how best to cluster patients with the same diseases. For the first goal, we will use internal in-house datasets of images (including facial images) to perform a comparison to “match” to known diseases in GMDB. We will use a variety of software...
Read moreDeveloping a Facial Recognition Tool for Genetic Disorder Diagnosis
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Principal Investigator: Dr. Mona Alshahrani
Institution: Saudi Data and Artificial Intelligence Authority (SDAIA) - National Center for Artificial Intelligence (NCAI)
Rare genetic disorders are often difficult to diagnose, with patients often undergoing years of testing and evaluation before a proper diagnosis is made. This can lead to delays in treatment and management, causing significant morbidity and mortality. In recent years, facial recognition technology and artificial intelligence have been used to aid in the diagnosis of rare genetic disorders, with...
Read moreExplore un/self-supervised applications in GMDB
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Principal Investigator: Prof. Bairong Shen
Institution: Institutes for Systems Genetics, West China Hospital
Deep learning models rely heavily on data, but in this area, GMDB is the only database and faces the difficulty of insufficient data. Meanwhile, face recognition databases have a large amount of data, and the similarity between GMDB and face recognition databases is that they are both facial images. Therefore, we try to train the model on unlabeled face recognition images first to learn facial...
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