2023
DOI: 10.1101/2023.06.06.23290887
|View full text |Cite
Preprint
|
Sign up to set email alerts
|

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

Hellen Lesmann,
Alexander Hustinx,
Shahida Moosa
et al.

Abstract: 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 impro… Show more

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
2
1

Citation Types

0
6
0

Year Published

2023
2023
2024
2024

Publication Types

Select...
5
1

Relationship

3
3

Authors

Journals

citations
Cited by 10 publications
(6 citation statements)
references
References 103 publications
0
6
0
Order By: Relevance
“…GestaltMatcher uses deep-learning algorithms to quantify facial image similarities to 7459 patients' images with 449 different disorders in GestaltMatcher Database (GMDB; Lesmann et al, 2023). In GMDB, there were 130 images with CSS.…”
Section: Ml-facial Phenotypingmentioning
confidence: 99%
“…GestaltMatcher uses deep-learning algorithms to quantify facial image similarities to 7459 patients' images with 449 different disorders in GestaltMatcher Database (GMDB; Lesmann et al, 2023). In GMDB, there were 130 images with CSS.…”
Section: Ml-facial Phenotypingmentioning
confidence: 99%
“…This limitation slows down the development of NGP for facial analysis. Therefore, GestaltMatcher Database (GMDB) was proposed as the first medical imaging database for rare disorders that fulfills the FAIR principle, which is findable, accessible, interoperable, and reusable (Lesmann, Lyon, et al, 2023). GMDB has been used to develop, and benchmark two NGP approaches (Hustinx et al, 2023;Sümer et al, 2023).…”
Section: Introductionmentioning
confidence: 99%
“…However, the training scale of the typical face recognition model is enormous, from 0.5 million images (CASIA-WebFace [Yi et al, 2014]) to 17 million images (Glint360K [An et al, 2021]). The Gestalt Score GestaltMatcher Database (Lesmann, Lyon, et al, 2023) https://db.gestaltmatcher.org/ Database FAIR medical imaging database enabling clinicians to browse patient images and providing a resource for developing the NGP approach.…”
Section: Introductionmentioning
confidence: 99%
See 1 more Smart Citation
“…E.g. via support from FAIR(Wilkinson et al, 2016) sources such as the GestaltMatcherDatabase(Lesmann et al, 2023).…”
mentioning
confidence: 99%