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.
Incorporating GestaltMatcher Database to refine a deep learning-based point-of-care screening tool for genetic syndromes in children
Nov. 19th 2024
Principal Investigator: Dr. Xinyang Liu
Institution: Children's National Hospital
Delays in the diagnosis of genetic syndromes are common, particularly in low and middle-income countries with limited access to genetic screening services. We have developed and evaluated a deep learning-based point-of-care screening tool for genetic syndromes in children. The tool analyzes an individual’s facial image to predict the likelihood of a syndromic condition. To enhance screening...
Read moreIntegrating GestaltMatcher Database with PubCaseFinder and BioHackathon in Japan
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Principal Investigator: Dr. Toyofumi Fujiwara
Institution: DBCLS
We would like to integrate the GestaltMatcher Database (GMDB) into our system, PubCaseFinder (Fujiwara et al. 2018) (https://pubcasefinder.dbcls.jp/). PubCaseFinder is a diagnostic support system for rare genetic diseases that has been in operation for eight years. It allows users to input patient symptoms using Human Phenotype Ontology (HPO) and obtain a ranked list of diseases based on...
Read moreImproving the support of next-generation phenotyping to Japanese patients
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Principal Investigator: Dr. Hiroyuki Mishima
Institution: Department of Human Genetics, Nagasaki University
The next-generation phenotyping technology, such as GestaltMatcher, has demonstrated the capability of diagnosing patients with rare disorders by analyzing patients' facial images. However, the majority of the training set in the GestaltMatcher Database is predominantly Caucasian. This ethnic bias in the training set may reduce performance when analyzing Asian patients. Therefore, this proposal...
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