The DOI matches its Crossref record; 47 of the 47 references it cites were found in a registry or an index.
GestaltMatcher facilitates rare disease matching using facial phenotype descriptors
Open accessPublished version of a preprint
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.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
The cited source says what the claim attributes to it.
In the citing paper
“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]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Richards, S., Aziz, N., Bale, S., Bick, D., Das, S., Gastier-Foster, J., Grody, W. W., Hegde, M., Lyon, E., Spector, E., et al. (2015) · Genetics in Medicine · doi:10.1038/gim.2015.30 · Cited by 32,594
Silhouettes: A graphical aid to the interpretation and validation of cluster analysis
Rousseeuw, P. J. (1987) · Journal of Computational and Applied Mathematics · doi:10.1016/0377-0427(87)90125-7 · Cited by 21,392
DeepFace: Closing the Gap to Human-Level Performance in Face Verification
Taigman, Y., Yang, M., Ranzato, M., & Wolf, L. (2014) · 2014 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2014.220 · Cited by 6,542
ClinVar: improving access to variant interpretations and supporting evidence
Landrum, M. J., Lee, J. M., Benson, M., Brown, G. R., Chao, C., Chitipiralla, S., Gu, B., Hart, J., Hoffman, D., Jang, W., et al. (2018) · Nucleic Acids Research · doi:10.1093/nar/gkx1153 · Cited by 4,196
Learning Face Representation from ScratchPreprint
Yi, D., Lei, Z., Liao, S., & Li, S. Z. (2014) · arXiv (Cornell University) · doi:10.48550/arxiv.1411.7923 · Cited by 2,282
Estimating cumulative point prevalence of rare diseases: analysis of the Orphanet database
Nguengang Wakap, S., Lambert, D. M., Olry, A., Rodwell, C., Gueydan, C., Lanneau, V., Murphy, D., Le Cam, Y., & Rath, A. (2020) · European Journal of Human Genetics · doi:10.1038/s41431-019-0508-0 · Cited by 1,752
GeneMatcher: a matching tool for connecting investigators with an interest in the same gene.
Sobreira, N., Schiettecatte, F., Valle, D., & Hamosh, A. (2015) · Human Mutation · doi:10.1002/humu.22844 · Cited by 1,529
GARCÍA-ALONSO, C. R., Pérez, L. M., & Fernández, J. C. F. (2011) · Annals of Operations Research · doi:10.1007/s10479-011-0841-3 · Cited by 1,435
Identifying facial phenotypes of genetic disorders using deep learning
Gurovich, Y., Hanani, Y., Bar, O., Nadav, G., Fleischer, N., Gelbman, D., Basel-Salmon, L., Krawitz, P. M., Kamphausen, S. B., Zenker, M., et al. (2019) · Nature Medicine · doi:10.1038/s41591-018-0279-0 · Cited by 739
Self-citationModeling the ACMG/AMP variant classification guidelines as a Bayesian classification framework
Tavtigian, S. V., Greenblatt, M. S., Harrison, S. M., Nussbaum, R. L., Prabhu, S. A., Boucher, K. M., & Biesecker, L. G. (2018) · Genetics in Medicine · doi:10.1038/gim.2017.210 · Cited by 590
Research works printed but not linked: 1
- van der Maaten, L. & Hinton, G. Visualizing data using t-SNE. J. Mach. Learn. Res. 9, 2579–2605 (2008).
Cited by
Sorted by citations
Items 101–101 of 101
Küry, S., Stanton, J. E., van Woerden, G. M., Bosc-Rosati, A., Hsieh, T.-C., Bray, L., Oloudé, M., Rosenfelt, C., Scott-Boyer, M. P., Most, V., et al. (2025) · Nature Communications · doi:10.1038/s41467-025-65556-8 · Cited by 3
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
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review