The neurodevelopmental and facial phenotype in individuals with a TRIP12 variant

Aerden, Mio; Denommé‐Pichon, Anne‐Sophie; Bonneau, Dominique; Bruel, Ange‐Line; Delanne, Julian; Gérard, Bénédicte; Mazel, Benoît; Philippe, Christophe; Pinson, Lucile; Prouteau, Clément; Putoux, Audrey; Mau-Them, Frédéric Tran; Viora‐Dupont, Eléonore; Vitobello, Antonio; Ziegler, Alban; Piton, Amélie; Isidor, Bertrand; Francannet, Christine; Maillard, Pierre‐Yves; Julia, Sophie; Philippe, Anaïs; Schaefer, Élise; Koene, Saskia; Ruivenkamp, Claudia; Hoffer, Mariëtte J.V.; Legius, Eric; Theunis, Miel; Keren, Boris; Buratti, Julien; Charles, Perrine; Courtin, Thomas; Misra-Isrie, Mala; Haelst, Mieke M. van; Waisfisz, Quinten; Wieczorek, Dagmar; Schmetz, Ariane; Herget, Theresia; Kortüm, Fanny; Lisfeld, Jasmin; Debray, François-Guillaume; Bramswig, Nuria C.; Atallah, Isis; Fodstad, Heidi; Jouret, Guillaume; Almoguera, Berta; Tahsin-Swafiri, Saoud; Santos‐Simarro, Fernando; Palomares‐Bralo, María; López‐González, Vanesa; Kibæk, Maria; Tørring, Pernille Mathiesen; Renieri, Alessandra; Bruno, Lucia Pia; Õunap, Katrin; Wojcik, Monica H.; Hsieh, Tzung-Chien; Krawitz, Peter; Esch, Hilde Van

doi:10.1038/s41431-023-01307-x

Cited by 11 publications

(13 citation statements)

References 22 publications

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“…For example, Ebstein et al showed that facial dysmorphism was heterogeneous among the entire PSMC3 patient cohort, but facial similarities were found in patients sharing the same pathogenic variants 34 . Deploying this method within our research platform has facilitated the quantification of similarity across 18 cohorts, with 15 already published 34,[44][45][46][47][48][49][50][51][52][53][54][55][56][57] .…”

Section: Facilitating Ngs Analysis With the Gestaltmatcher Api And Re...mentioning

confidence: 99%

GestaltMatcher Database - A global reference for facial phenotypic variability in rare human diseases

Lesmann,

Hustinx,

Moosa

et al. 2023

Preprint

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The value of computer-assisted image analysis has been shown in several studies. The performance of tools with artificial intelligence (AI), such as GestaltMatcher, is improved with the size and diversity of the training set, but properly labeled training data is currently the biggest bottleneck in developing next-generation phenotyping (NGP) applications. Therefore, we developed GestaltMatcher Database (GMDB) - a database for machine-readable medical image data that complies with the FAIR principles and improves the openness and accessibility of scientific findings in Medical Genetics. An entry in GMDB consists of a medical image such as a portrait, X-ray, or fundoscopy, and machine-readable meta information such as a clinical feature encoded in HPO terminology or a disease-causing mutation reported in HGVS format. In the beginning, data was mainly collected by curators gathering images from the literature. Currently, clinicians and individuals recruited from patient support groups provide their previously unpublished data. For this patient-centered approach, we developed a digital consent form. GMDB is a modern publication medium for case reports that complements preprints, e.g., on medRxiv. To enable inter-cohort comparisons, we implemented a research feature in GMDB that computes the pairwise syndromic similarity between hand-picked cases. Through a community-driven effort, we compiled an image collection of over 7,533 cases with 792 disorders in GMDB. Most of the data was collected from 2,058 publications. In addition, about 1,018 frontal images of 498 previously unpublished cases were obtained. The web interface enables gene- and phenotype-centered queries or infinite scrolls in the gallery. Digital consent has led to increasing adoption of the approach by patients. The research app within GMDB was used to generate syndromic similarity matrices to characterize two novel phenotypes (CSNK2B, PSMC3). GMDB is the first FAIR database for NGP, where data are findable, accessible, interoperable, and reusable. It is a repository for medical images that cannot be included in medRxiv. That means GMDB connects clinicians with a shared interest in particular phenotypes and improves the performance of AI.

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Section: Facilitating Ngs Analysis With the Gestaltmatcher Api And Re...mentioning

confidence: 99%

GestaltMatcher Database - A global reference for facial phenotypic variability in rare human diseases

Lesmann,

Hustinx,

Moosa

et al. 2023

Preprint

Self Cite

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show abstract

“…With the recent advances in computer vision, many AI‐based applications have begun to be used to facilitate diagnosis through facial image analysis (Dingemans et al., 2022; Dudding‐Byth et al., 2017; Gurovich et al., 2019; Hsieh et al., 2022; Hustinx et al., 2023; Porras et al., 2021; Sümer et al., 2023) and to further delineate the phenotypes of novel diseases (Aerden et al., 2023; Asif et al., 2022; Averdunk et al., 2023; Ebstein et al., 2023; Guo et al., 2022; Kampmeier et al., 2022; Knaus et al., 2018; Lyon et al., 2023; Marbach et al., 2019; Oppermann et al., 2023; Pantel et al., 2018).…”

Section: Commentarymentioning

confidence: 99%

Facilitating the Molecular Diagnosis of Rare Genetic Disorders Through Facial Phenotypic Scores

Hsieh,

Lesmann,

Krawitz

2023

Current Protocols

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With recent advances in computer vision, many applications based on artificial intelligence have been developed to facilitate the diagnosis of rare genetic disorders through the analysis of patients’ two‐dimensional frontal images. Some of these have been implemented on online platforms with user‐friendly interfaces and provide facial analysis services, such as Face2Gene. However, users cannot run the facial analysis processes in house because the training data and the trained models are unavailable. This article therefore provides an introduction, designed for users with programming backgrounds, to the use of the open‐source GestaltMatcher approach to run facial analysis in their local environment. The Basic Protocol provides detailed instructions for applying for access to the trained models and then performing facial analysis to obtain a prediction score for each of the 595 genes in the GestaltMatcher Database. The prediction results can then be used to narrow down the search space of disease‐causing mutations or further connect with a variant‐prioritization pipeline. © 2023 The Authors. Current Protocols published by Wiley Periodicals LLC.Basic Protocol: Using the open‐source GestaltMatcher approach to perform facial analysis

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“…Later on, many genes were analyzed by GestaltMatcher (Aerden et al, 2023;Blackburn et al, 2023;Ebstein et al, 2023;Guo et al, 2022). Instead of only visualizing the pairwise comparison, Blackburn et al presented an objective measurement showing that 53.72% of the random sampling of CUL3 patients presented a similar facial phenotype.…”

Section: Measuring Facial Gestalt Of a Disordermentioning

confidence: 99%

“…The clustering dendrogram further revealed that patients 1, 2, 3, and 7 with the new IDCS formed a cluster different from patients 4, 5, and 6, which manifests POBINDS. Later on, many genes were analyzed by GestaltMatcher (Aerden et al, 2023; Blackburn et al, 2023; Ebstein et al, 2023; Guo et al, 2022). Instead of only visualizing the pairwise comparison, Blackburn et al presented an objective measurement showing that 53.72% of the random sampling of CUL3 patients presented a similar facial phenotype.…”

Section: Facial Phenotype Descriptors Delineate Facial Gestalt Of Dis...mentioning

confidence: 99%

Computational facial analysis for rare Mendelian disorders

Hsieh

Krawitz

2023

American J of Med Genetics Pt C

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With the advances in computer vision, computational facial analysis has become a powerful and effective tool for diagnosing rare disorders. This technology, also called next‐generation phenotyping (NGP), has progressed significantly over the last decade. This review paper will introduce three key NGP approaches. In 2014, Ferry et al. first presented Clinical Face Phenotype Space (CFPS) trained on eight syndromes. After 5 years, Gurovich et al. proposed DeepGestalt, a deep convolutional neural network trained on more than 21,000 patient images with 216 disorders. It was considered a state‐of‐the‐art disorder classification framework. In 2022, Hsieh et al. developed GestaltMatcher to support the ultra‐rare and novel disorders not supported in DeepGestalt. It further enabled the analysis of facial similarity presented in a given cohort or multiple disorders. Moreover, this article will present the usage of NGP for variant prioritization and facial gestalt delineation. Although NGP approaches have proven their capability in assisting the diagnosis of many disorders, many limitations remain. This article will introduce two future directions to address two main limitations: enabling the global collaboration for a medical imaging database that fulfills the FAIR principles and synthesizing patient images to protect patient privacy. In the end, with more and more NGP approaches emerging, we envision that the NGP technology can assist clinicians and researchers in diagnosing patients and analyzing disorders in multiple directions in the near future.

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The neurodevelopmental and facial phenotype in individuals with a TRIP12 variant

Cited by 11 publications

References 22 publications

GestaltMatcher Database - A global reference for facial phenotypic variability in rare human diseases

GestaltMatcher Database - A global reference for facial phenotypic variability in rare human diseases

Facilitating the Molecular Diagnosis of Rare Genetic Disorders Through Facial Phenotypic Scores

Computational facial analysis for rare Mendelian disorders

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