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

Abstract

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 We collected 10,980 frontal facial images - more than a quarter previously unpublished - from 8,346 patients, representing 581 rare disorders.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 This includes previously unpublished reports for more than 40% of the African patients.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.

Figure 1:

a) Birth rate distribution worldwide. The size of country is scaled in accordance with the respective birth rate. The map indicates countries from which unpublished images were obtained (source: https://worldmapper.org/faq/, modified). b) Distribution of ancestry groups in GestaltMatcher Database. 16% of the patients without ancestral information were categorized as Unknown. The breakdown of ancestries in the dataset with known ancestry is as follows: European 67%, Asian 19%, African 7%, and Others 7%.

Figure 2:. GestaltMatcher Database (GMDB) Architecture and Dataflow.

a) Retrospective data are collected from the literature and annotated by data curators or are uploaded by collaborating attending clinician. Patients can also upload images of their own cases, incorporate prospective data, and view their own data at any time. b) The data (multimodal image data, including portrait images as well as magnetic resonance imaging, X-ray, fundscopy and extremity images) are stored in the GMDB (MySQL database) together with the relevant meta information (such as sex, age, ancestry, molecular, and phenotypic information). c) Registered users can view and search the FAIR data in the GMDB Gallery. The patient image can also be analyzed using the Next-Generation Phenotyping tool GestaltMatcher within the Research Platform. In addition, once their application has been approved by the Advisory Board, external computer scientists can use the GMDB-FAIR data set for training purposes for their projects.

Figure 3:. An example case presentation of a FAIR case with a Digital Object Identifier (DOI).

a) FAIR cases in the GestaltMatcher Database (GMDB) are displayed to GMDB users via the data sheet. Each FAIR case can also be assigned a DOI in order to render it a citable micro-publication. This micro-publication contains the image data and metadata, including demographic, molecular, and phenotype information. The dynamic nature of the GMDB case report enables longitudinal image data storage even after initial publication, which is not possible in conventional journals. b) After uploading, case reports can be viewed and searched by other users in the Gallery view. c) The image data can also be used for inter-cohort comparisons of the gestalt scores within the research platform.

Figure 4:. Overview of the GestaltMatcher Database (GMDB)-FAIR dataset.

a) Sex distribution. Number of images shown in brackets. b) Distribution of patient age in years. c) Left: Two-dimensional representation of phenotypic similarities between patients, as calculated on the basis of Human Phenotype Ontology (HPO) terms via Uniform Manifold Approximation and Projection (UMAP). HPO terms were annotated for 4,474 individuals in the GMDB, and expert clinicians defined twelve distinct HPO-defined symptom groups. Based on the annotated HPO terms, each case was assigned to one or more HPO-defined symptom groups. All OMIM diseases were included, using their HPO annotations (gray background dots) as a reference. GMDB cases are color-coded according to their most pronounced HPO-defined symptom group, i.e., the group that includes the majority of their HPO terms. The dataset is dominated by two major clusters (facial dysmorphism in yellow and neurodevelopmental in blue) but shows cases from across the complete disease landscape. Right: Heatmap of the proportion of GMDB individuals within the HPO-defined symptom group on the X-axis who are also assigned to the HPO-defined symptom group on the Y-axis. Notably, facial dysmorphism is present in at least 70% of the cases of each HPO-defined symptom group. d) Proportion of the unpublished and published images in each ancestry group. e) Proportion of the unpublished and published images in each sub-ancestry group.

Figure 5:. Performance of ancestry analysis.

a) Top-1 and top-5 accuracy of GestaltMatchers’ disorder classification accuracy per ancestral group. Top-1 and top-5 accuracy of the models’ disorder classification accuracy per ancestral group, where (blue) belongs to the EU only subset, and (yellow) belongs to the diverse subset. Each wide, darker bar and each light, thinner bar indicate the top-1 and top-5 accuracy per ancestral group, respectively. The horizontal dashed lines and dotted lines indicate the top-1 and top-5 overall accuracy averaged over all ancestral groups, respectively. The order of the ancestry group in the x-axis is ranked according to standard deviation between top-1 accuracies of the 5-fold experiment. b) Top-1 accuracy of GestaltMatcher when including different proportion of non-European patients in the gallery. The x-axis is the proportion of non-European data included in the gallery. The y-axis is the top-1 accuracy. The colored region along the line indicates the standard deviation.