medRxiv : the preprint server for health sciences · 2024

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

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

Аннотация

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.

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

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