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GestaltMatcher facilitates rare disease matching using facial phenotype descriptors
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Abstract
Many monogenic disorders cause a characteristic facial morphology. Artificial intelligence can support physicians in recognizing these patterns by associating facial phenotypes with the underlying syndrome through training on thousands of patient photographs. However, this 'supervised' approach means that diagnoses are only possible if the disorder was part of the training set. To improve recognition of ultra-rare disorders, we developed GestaltMatcher, an encoder for portraits that is based on a deep convolutional neural network. Photographs of 17,560 patients with 1,115 rare disorders were used to define a Clinical Face Phenotype Space, in which distances between cases define syndromic similarity. Here we show that patients can be matched to others with the same molecular diagnosis even when the disorder was not included in the training set. Together with mutation data, GestaltMatcher could not only accelerate the clinical diagnosis of patients with ultra-rare disorders and facial dysmorphism but also enable the delineation of new phenotypes.
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“Nonetheless, facial analysis with GestaltMatcher17 suggested a recognizable facial gestalt among PSMC5 subjects.”
From Investigating the neuronal role of the proteasomal ATPase subunit gene PSMC5 in neurodevelopmental proteasomopathies (2025) · cites this paper as [17]
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Self-citation
Metadata
- Authors
- Hsieh, T.-C., Bar-Haim, A., Moosa, S., Ehmke, N., Gripp, K. W., Pantel, J. T., Danyel, M., Mensah, M. A., Horn, D., Rosnev, S., Fleischer, N., Bonini, G., Hustinx, A., Schmid, A., Knaus, A., Javanmardi, B., Klinkhammer, H., Lesmann, H., Sivalingam, S., Kamphans, T., Meiswinkel, W., Ebstein, F., Krüger, E., Küry, S., Bézieau, S., Schmidt, A., Peters, S., Engels, H., Mangold, E., Kreiß, M., Cremer, K., Perne, C., Betz, R. C., Bender, T., Grundmann-Hauser, K., Haack, T. B., Wagner, M., Brunet, T., Bentzen, H. B., Averdunk, L., Coetzer, K. C., Lyon, G. J., Spielmann, M., Schaaf, C. P., Mundlos, S., Nöthen, M. M., & Krawitz, P. M.
- Affiliations
- University of Bonn (DE)
- Stellenbosch University (ZA)
- Humboldt-Universität zu Berlin (DE)
- Alfred I. duPont Hospital for Children (US)
- Universitätsmedizin Greifswald (DE)
- Centre National de la Recherche Scientifique (FR)
- University of Tübingen (DE)
- Helmholtz Munich (DE)
- University of Oslo (NO)
- Düsseldorf University Hospital (DE)
- The Graduate Center, CUNY (US)
- University of Lübeck (DE)
- Heidelberg University (DE)
- Corresponding author
- Krawitz, Peter M.
- ORCID
- Hsieh, Tzung-Chien
- Bar-Haim, Aviram
- Moosa, Shahida
- Ehmke, Nadja
- Gripp, Karen W.
- Pantel, Jean Tori
- Danyel, Magdalena
- Mensah, Martin Atta
- Horn, Denise
- Rosnev, Stanislav
- Fleischer, Nicole
- Hustinx, Alexander
- Knaus, Alexej
- Javanmardi, Behnam
- Klinkhammer, Hannah
- Lesmann, Hellen
- Sivalingam, Sugirthan
- Kamphans, Tom
- Ebstein, Frédéric
- Krüger, Elke
- Küry, Sébastien
- Bézieau, Stéphane
- Schmidt, Axel
- Peters, Sophia
- Engels, Hartmut
- Mangold, Elisabeth
- Perne, Claudia
- Betz, Regina C.
- Haack, Tobias B.
- Wagner, Matias
- Brunet, Theresa
- Bentzen, Heidi Beate
- Averdunk, Luisa
- Coetzer, Kimberly Christine
- Lyon, Gholson J.
- Spielmann, Malte
- Schaaf, Christian P.
- Mundlos, Stefan
- Nöthen, Markus M.
- Krawitz, Peter M.
- Venue
- Nature Genetics
- Published
- 2022-02-10
- Volume
- 54
- Issue
- 3
- Pages
- 349-357
- ISSN
- 1061-4036 (ISSN-L)
- 1546-1718
- PMC
- PMC9272356
- OpenAlex
- W4211105116
- Cited by
- 233
- References
- 45
- Citations by year
- 2026: 55 · 2025: 56 · 2024: 48 · 2023: 52 · 2022: 21
- FWCI
- 26.68
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Nature Genetics (publishedVersion)
- medRxiv (acceptedVersion, cc-by-nc-nd)
- Research Square (acceptedVersion, cc-by)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- HAL (submittedVersion)
- mediaTUM – the media and publications repository of the Technical University Munich (Technical University Munich) (submittedVersion)
- Site cant be reached (submittedVersion, other-oa)
- PuSH - Publication Server of Helmholtz Zentrum München (submittedVersion)
- Funders
- Deutsche Forschungsgemeinschaft (KR 3985/6-1, KR 3985/7-3)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genomics and Rare Diseases
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Face recognition and analysis
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Keywords
- rare disease diagnosis, facial phenotype, facial dysmorphism, monogenic disorders, facial morphology, deep convolutional neural networks, patient matching, phenotypic similarity, disease gene discovery, diagnosis
- MeSH terms
- Artificial Intelligence, Face, Humans, Phenotype, Neural Networks, Computer, Rare Diseases
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