Ongoing Projects

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...

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...

Principal Investigator: Dr. Mete Akgün

Institution: University of Tübingen

The primary focus is developing privacy-preserving machine learning techniques for handling medical data. One of our key areas of interest is rare disease analysis, which requires access to a substantial number of genomes for successful research. However, obtaining access to numerous genomes presents significant privacy challenges, particularly in compliance with regulations like GDPR. We...

Principal Investigator: Prof. Ariane Machado-Lima

Institution: University of Sao Paulo

My research team aims to investigate facial anthropometric patterns in some disorders to assist in diagnosis. While our primary interest is in Autism Spectrum Disorder, we are also interested in exploring other syndromes to evaluate our models. We would greatly appreciate the opportunity to analyse GMDB data.

Principal Investigator: Prof. Sowmya Kamath

Institution: National Institute of Technology Karnataka, Surathkal, India

We are currently working on developing advanced AI models for the diagnosis of Genetic Syndromes (GS) through facial gestalt analysis. Facial gestalt, which focuses on recognizing specific dysmorphic features in the face, is a valuable diagnostic tool for identifying various GS. However, current methodologies that rely on geometric data, facial landmarks, and handcrafted features are limited by...

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...

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...

Principal Investigator: Prof. Gemma Piella

Institution: Universitat Pompeu Fabra

Congenital anomalies are a major cause of infant mortality and childhood morbidity, affecting 2-3% of newborns. It is estimated that 30% to 40% of genetic disorders produce alterations in the normal morphology of the face and the head (dysmorphology), which can impact swallowing, breathing, hearing, vision, speech, and -more importantly- cognitive development. Thus, craniofacial anomalies have...

Principal Investigator: Mr. Jonathan Campbell

Institution: University of Oxford

The classification of genetic disorders from face images has the potential to assist with early diagnosis and effective treatment. A key objective in this field is to enhance the accuracy and robustness of deep learning models in classifying genetic disorders from face images for disorders that are not represented in the training data. In this paper, we propose the use of input mixup...

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...

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...

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...

Principal Investigator: Mr. Marcelo Martinez Conti

Institution: Foundation 29

The current diagnostic process for rare diseases is often inefficient and reactive, heavily relying on parental initiative and persistence. This prolonged and arduous process not only affects the physical health of patients but also imposes substantial emotional and financial burdens on their families. Project VALEN is a non-profit initiative which aims to address this challenge by...

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...

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...

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...

Principal Investigator: Mr. Andrew Pregnall

Institution: University of Pennsylvania

Background: Syndromic genetic disorders often present with distinct cutaneous and/or facial findings that facilitate the diagnostic process. Specially trained machine learning algorithms such as DeepGestalt can obtain over 90% top-10 accuracy in diagnosing syndromic conditions from facial image analysis alone. Large-language models have recently emerged as powerful systems for evaluating...

Principal Investigator: Dr. Zirui Dong

Institution: The Chinese University of Hong Kong

Women’s reproductive health underpins population sustainability. In China, infertility has risen from 11.9 % (2007) to 17.6 % (2020), driven by later marriages and limited support, which urgently needs faster, earlier screening to cut misdiagnosis and protect fertility. Endocrine disorders—premature ovarian insufficiency, polycystic ovary syndrome, hypothalamic amenorrhoea, hyperprolactinaemia,...