Parents’ perspectives on the use of children’s facial images for research and diagnosis: a survey

Schoeman, Lize; Honey, Engela; Malherbe, Helen; Coetzee, Vinet

doi:10.1007/s12687-022-00612-0

Cited by 2 publications

(6 citation statements)

References 24 publications

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“…During the GestaltMatcher consent procedure, patients can also indicate whether they agree to the use of the images in presentations, teaching activities, or in publications in other journals. This differentiation from other journals is important since patients/parents show less willingness to consent to publication in open-access journals than to publication in access-controlled databases that are not publicly accessible 67 . The patient shown in Figure 3 fully consented to publication of his image data.…”

Section: Methodsmentioning

confidence: 99%

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

Hsieh,

Lesmann,

Hustinx

et al. 2024

Preprint

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 We collected 10,980 frontal facial images – more than a quarter previously unpublished - from 8,346 patients, representing 581 rare disorders.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 This includes previously unpublished reports for more than 40% of the African patients.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

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Section: Methodsmentioning

confidence: 99%

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

Hsieh,

Lesmann,

Hustinx

et al. 2024

Preprint

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show abstract

“…We introduced this distinction because it is known that patient consent to data sharing is higher when not shared with a broad mass, but only for a specific study 67 . However, many patients agree to controlled access for the general scientific community to advance research 67 . For this reason, patients can decide whether they want to be part of only the GMDB-private set for AI training or agree to be part of the FAIR data set.…”

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

“…But actually, the GMDB consists of the GMDB-FAIR dataset and the GMDB-private set (Supplementary Note and Supplementary Figures 7 and 8). We introduced this distinction because it is known that patient consent to data sharing is higher when not shared with a broad mass, but only for a specific study 67 . However, many patients agree to controlled access for the general scientific community to advance research 67 .…”

Section: Gmdb Datasetsmentioning

confidence: 99%

Section: Gmdb Datasetsmentioning

confidence: 99%

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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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Parents’ perspectives on the use of children’s facial images for research and diagnosis: a survey

Cited by 2 publications

References 24 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

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

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