Ongoing Projects
Photorealistic Synthetic Portraits
Here we show photorealistic synthetic portraits of certain rare diseases based on the cohort ... 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 8 active research projects approved since 2022 until today.
Integrating GestaltMatcher Database with PubCaseFinder and BioHackathon in Japan
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
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...
Read moreExplore un/self-supervised applications in GMDB
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...
Read moreA survey on facial feature extraction methods in a small data environment: the diagnosis of rare genetic syndromes
Principal Investigator: Mr. Lex Dingemans
Institution: Radboudumc
We would like to include the pretrained version of GestaltMatcher-arc in our survey study. This would require us to have access to the weights of the model, as published in previous work (GestaltMatcher-Arc). Of course, we will not (re)share these with anyone else outside our research group. For now, we do not aim to (re)train the model or use transfer learning, but are purely interested in the...
Read moreIncorporating 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 moreAutomatic Phenotyping of Patients on 2D Photography (AIDY2)
Mar. 10th 2025
Principal Investigator: Prof. ahmed zaiter
Institution: EMLyon
In our AIDY project, we have already developed AI algorithms for recognizing genetic syndromes by combining frontal, profile, and ear photos for a few selected syndromes. Now, we want to increase the number of genetic syndromes to be recognized, so we will develop a new algorithmic methodology using state-of-the-art algorithms capable of handling a large amount of data without spending too much...
Read moreAnatomy-Informed Artificial Intelligence for Rare Disease Visual Ontology Creation
Apr. 16th 2025
Principal Investigator: Prof. Aaron Masino
Institution: Clemson University
Artificial intelligence (AI) has the potential to accelerate rare disease research and diagnosis, which remains challenging. However, the deep learning models underpinning modern AI systems function as "black boxes" that provide accurate predictions without explaining their conclusions. In this project, we aim to develop AI methods informed by facial anatomy to create a hierarchical ontology of...
Read moreDevelopment of a Multimodal Large Language Model (MLLM) for Early Screening of Rare Genetic Disorders in Children
Jun. 25th 2025
Principal Investigator: Dr. Yue Yang
Institution: Renmin hospital of Wuhan University
This project aims to develop a non-invasive, AI-driven screening system using Multimodal Large Language Models (MLLMs) to identify rare genetic disorders in children through facial and physical phenotype analysis. The system will integrate high-resolution imagery, 3D anthropometric data, and clinical metadata to improve early detection of conditions such as Noonan syndrome, Marfan syndrome...
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