Dysmorphologists sometimes encounter challenges in recognizing disorders due to phenotypic variability influenced by factors such as age and ethnicity. Moreover, the performance of Next Generation Phenotyping Tools such as GestaltMatcher is dependent on the diversity of the training set. Therefore, we developed GestaltMatcher Database (GMDB) - a global reference for the phenotypic variability of rare diseases that complies with the FAIR-principles. We curated dysmorphic patient images and metadata from 2,224 publications, transforming GMDB into an online dynamic case report journal. To encourage clinicians worldwide to contribute, each case can receive a Digital Object Identifier (DOI), making it a citable micro-publication. This resulted in a collection of 2,312 unpublished images, partly with longitudinal data. We have compiled a collection of 10,189 frontal images from 7,695 patients representing 683 disorders. The web interface enables gene- and phenotype-centered queries for registered users (https://db.gestaltmatcher.org/). Despite the predominant European ancestry of most patients (59%), our global collaborations have facilitated the inclusion of data from frequently underrepresented ethnicities, with 17% Asian, 4% African, and 6% with other ethnic backgrounds. The analysis has revealed a significant enhancement in GestaltMatcher performance across all ethnic groups, incorporating non-European ethnicities, showcasing a remarkable increase in Top-1-Accuracy by 31.56% and Top-5-Accuracy by 12.64%. Importantly, this improvement was achieved without altering the performance metrics for European patients. GMDB addresses dysmorphology challenges by representing phenotypic variability and including underrepresented groups, enhancing global diagnostic rates and serving as a vital clinician reference database.

中文翻译：

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GestaltMatcher 数据库 - 人类罕见疾病面部表型变异的全球参考

形态异常学家有时在识别由于年龄和种族等因素影响的表型变异而导致的疾病时遇到挑战。此外，GestaltMatcher 等下一代表型分析工具的性能取决于训练集的多样性。因此，我们开发了 GestaltMatcher 数据库（GMDB）——符合 FAIR 原则的罕见疾病表型变异的全球参考。我们从 2,224 份出版物中整理了畸形患者图像和元数据，将 GMDB 转变为在线动态病例报告期刊。为了鼓励世界各地的临床医生做出贡献，每个病例都可以获得一个数字对象标识符 (DOI)，使其成为可引用的微型出版物。最终收集了 2,312 张未发表的图像，其中部分包含纵向数据。我们收集了来自 7,695 名患者的 10,189 张正面图像，代表了 683 种疾病。Web 界面可为注册用户提供以基因和表型为中心的查询 (https://db.gestaltmatcher.org/)。尽管大多数患者（59%）主要是欧洲血统，但我们的全球合作促进了纳入代表性不足的种族的数据，其中 17% 为亚洲人，4% 为非洲人，6% 为其他种族背景。分析显示，所有种族（包括非欧洲种族）的 GestaltMatcher 性能均显着提高，Top-1 准确率显着提高了 31.56%，Top-5 准确率显着提高了 12.64%。重要的是，这种改进是在没有改变欧洲患者的表现指标的情况下实现的。GMDB 通过代表表型变异性并纳入代表性不足的群体、提高全球诊断率并作为重要的临床医生参考数据库来解决形态异常挑战。