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

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Authors: Hellen Lesmann, Alexander Hustinx, Shahida Moosa, Hannah Klinkhammer, Elaine Marchi, Pilar Caro, Ibrahim M Abdelrazek, Jean Tori Pantel, Merle Ten Hagen, Meow-Keong Thong, Rifhan Azwani Binti Mazlan, Sok Kun Tae, Tom Kamphans, Wolfgang Meiswinkel, Jing-Mei Li, Behnam Javanmardi, Alexej Knaus, Annette Uwineza, Cordula Knopp, Tinatin Tkemaladze, Miriam Elbracht, Larissa Mattern, Rami Abou Jamra, Clara Velmans, Vincent Strehlow, Maureen Jacob, Angela Peron, Cristina Dias, Beatriz Carvalho Nunes, Thainá Vilella, Isabel Furquim Pinheiro, Chong Ae Kim, Maria Isabel Melaragno, Hannah Weiland, Sophia Kaptain, Karolina Chwiałkowska, Miroslaw Kwasniewski, Ramy Saad, Sarah Wiethoff, Himanshu Goel, Clara Tang, Anna Hau, Tahsin Stefan Barakat, Przemysław Panek, Amira Nabil, Julia Suh, Frederik Braun, Israel Gomy, Luisa Averdunk, Ekanem Ekure, Gaber Bergant, Borut Peterlin, Claudio Graziano, Nagwa Gaboon, Moisés Fiesco-Roa, Alessandro Mauro Spinelli, Nina-Maria Wilpert, Prasit Phowthongkum, Nergis Güzel, Tobias B Haack, Rana Bitar, Andreas Tzschach, Agusti Rodriguez-Palmero, Theresa Brunet, Sabine Rudnik-Schöneborn, Silvina Noemi Contreras-Capetillo, Ava Oberlack, Carole Samango-Sprouse, Teresa Sadeghin, Margaret Olaya, Konrad Platzer, Artem Borovikov, Franziska Schnabel, Lara Heuft, Vera Herrmann, Renske Oegema, Nour Elkhateeb, Sheetal Kumar, Katalin Komlosi, Khoushoua Mohamed, Silvia Kalantari, Fabio Sirchia, Antonio F Martinez-Monseny, Matthias Höller, Louiza Toutouna, Amal Mohamed, Amaia Lasa-Aranzasti, John A Sayer, Nadja Ehmke, Magdalena Danyel, Henrike Sczakiel, Sarina Schwartzmann, Felix Boschann, Max Zhao, Ronja Adam, Lara Einicke, Denise Horn, Kee Seang Chew, Choy Chen Kam, Miray Karakoyun, Ben Pode-Shakked, Aviva Eliyahu, Rachel Rock, Teresa Carrion, Odelia Chorin, Yuri A Zarate, Marcelo Martinez Conti, Mert Karakaya, Moon Ley Tung, Bharatendu Chandra, Arjan Bouman, Aime Lumaka, Naveed Wasif, Marwan Shinawi, Patrick R Blackburn, Tianyun Wang, Tim Niehues, Axel Schmidt, Regina Rita Roth, Dagmar Wieczorek, Ping Hu, Rebekah L Waikel, Suzanna E Ledgister Hanchard, Gehad Elmakkawy, Sylvia Safwat, Frédéric Ebstein, Elke Krüger, Sébastien Küry, Stéphane Bézieau, Annabelle Arlt, Eric Olinger, Felix Marbach, Dong Li, Lucie Dupuis, Roberto Mendoza-Londono, Sofia Douzgou Houge, Denisa Weis, Brian Hon-Yin Chung, Christopher C Y Mak, Hülya Kayserili, Nursel Elcioglu, Ayca Aykut, Peli Özlem Şimşek-Kiper, Nina Bögershausen, Bernd Wollnik, Heidi Beate Bentzen, Ingo Kurth, Christian Netzer, Aleksandra Jezela-Stanek, Koen Devriendt, Karen W Gripp, Martin Mücke, Alain Verloes, Christian P Schaaf, Christoffer Nellåker, Benjamin D Solomon, Markus M Nöthen, Ebtesam Abdalla, Gholson J Lyon, Peter M Krawitz, Tzung-Chien Hsieh

The most important factor that complicates the work of dysmorphologists is the significant phenotypic variability of the human face. Next-Generation Phenotyping (NGP) tools that assist clinicians with recognizing characteristic syndromic patterns are particularly challenged when confronted with patients from populations different from their training data. To that end, we systematically analyzed the impact of genetic ancestry on facial dysmorphism. For that purpose, we established the GestaltMatcher Database (GMDB) as a reference dataset for medical images of patients with rare genetic disorders from around the world. We collected 10,980 frontal facial images - more than a quarter previously unpublished - from 8,346 patients, representing 581 rare disorders. Although the predominant ancestry is still European (67%), data from underrepresented populations have been increased considerably via global collaborations (19% Asian and 7% African). This includes previously unpublished reports for more than 40% of the African patients. The NGP analysis on this diverse dataset revealed characteristic performance differences depending on the composition of training and test sets corresponding to genetic relatedness. For clinical use of NGP, incorporating non-European patients resulted in a profound enhancement of GestaltMatcher performance. The top-5 accuracy rate increased by +11.29%. Importantly, this improvement in delineating the correct disorder from a facial portrait was achieved without decreasing the performance on European patients. By design, GMDB complies with the FAIR principles by rendering the curated medical data findable, accessible, interoperable, and reusable. This means GMDB can also serve as data for training and benchmarking. In summary, our study on facial dysmorphism on a global sample revealed a considerable cross ancestral phenotypic variability confounding NGP that should be counteracted by international efforts for increasing data diversity. GMDB will serve as a vital reference database for clinicians and a transparent training set for advancing NGP technology.

PMID: 38903062

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