AI-Based Next-Generation-Phenotyping for Rare Disease Diagnosis

Artificial Intelligence (AI) can help to address these challenges on various levels. One particularly interesting approach is Next-Generation-Phenotyping (NGP) where AI is used to recognize disease-specific phenotypic patterns, such as distinct facial features, that are associated with rare diseases, to predict disease diagnosis and inform genetic testing. NGP tools can thus benefit patients, physicians, and drug developers by enhancing speed and accuracy of diagnosis, deepening disease understanding, and accelerating recruitment for clinical trials.

This article marks the launch of a new monthly column by Dr. Louise von Stechow on how emerging technologies—artificial intelligence, gene therapies, next-generation phenotyping, and more—are helping address challenges in diagnosing, treating, and managing rare diseases.

Today's piece is co-authored by Alexander Hustinx, a PhD candidate and deep learning engineer, and Dr. Adele Ruder, a biomedical scientist and medical science liaison—both based at the Institute for Genomic Statistics and Bioinformatics (IGSB), University of Bonn, and contributors to the GestaltMatcher and Bone2Gene initiatives.

NGP for Rare Disease Diagnosis 

Around 80% of the over 6000 rare diseases have a genetic origin, many of which are manifested in distinct phenotypic patterns. For example, it is estimated that up to 40% of rare disease patients have distinct facial features. Similarly, unique skeletal patterns are often associated with rare skeletal disorders, of which over 700 distinct diseases have been classified. 

For a long time, physicians had to rely on visual cues alone, from looking at patients’ faces, body morphology, skeletal radiographs or other medical images to translate the observations of distinct phenotypes into potential diagnoses. While experienced dysmorphologists develop an expert eye for identifying certain rare genetic disorders based on subtle cues, the overwhelming number of rare diseases, their individual rarity and inter-individual variation make it hard for most human clinicians to identify distinguishing patterns.  

NGP tools can now help bridge those gaps with AI-based analysis of medical images to detect patterns in a less subjective way than humans, and help clinicians derive diagnoses faster and with higher accuracy. While originally mainly focused on facial recognition (FR) technologies, academic groups and biotech companies are now also applying NGP to other types of medical images such as skeletal radiographs and retinal images. 

However, FR remains the furthest advanced technology among the set of NGP applications for rare disease diagnosis. While clinical utility of FR for diagnosing rare diseases was already shown in 2014, more recently, the accuracy and applicability of FR tools have increased with advanced Deep Learning methods. 

Examples of FR-based analysis platforms include US-based FDNA’s Face2Gene (both a platform and an app, used in over 10,000 medical centers to analyze over 550,000 cases worldwide by April 2025, as listed on the company’s homepage), Australian FaceMatch which directly targets parents of undiagnosed children and adults with known genetic conditions, UK-based ClinFace that focuses on 3D patient photos, and German GestaltMatcher, also available as a platform and an app for clinicians. 

Other NGP applications go beyond facial recognition and focus on other medical images or integration of different data sources. For example, Bone2Gene AI, funded by the German Federal Ministry of Education and Research, aims to identify the unique patterns in radiographic images linked to various bone disorders and support clinicians in the diagnostic process. Other academic applications include PhenoScore, an open-source AI framework that combines FR with Human Phenotype Ontology (HPO) data to quantify phenotypic similarity, to identify known and novel phenotypic subgroups linked to specific genetic variants. 

Clinical Use of NGP tools – The Example of GestaltMatcherAI

Generally, for rare disease diagnosis, FR tools compare a photo of the patient's face to a dataset of cases to find patterns within the facial features that can lead to a potential diagnosis and point toward genetic tests. For example, GestaltMatcher AI, available as an app, allows clinicians to analyze patient photos directly from a phone or tablet. In the following steps, the tool provides a scored list of possible diagnoses that help guide the selection and interpretation of genetic tests.