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VERSION:2.0
PRODID:https://github.com/derhansen/sf_event_mgt
METHOD:PUBLISH
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UID:6439-409@gi.de
CLASS: PUBLIC
SUMMARY:GESTALTMATCHER - Next-generation phenotyping for rare genetic disor
 ders
DESCRIPTION:Worldwide, rare genetic disorders affect more than 6.2% of the 
 population. The long diagnostic process is often called the ‘diagnostic ody
 ssey.’ With the recent advances in computer vision, many next-generation ph
 enotyping (NGP) approaches such as DeepGestalt have shown a strong ability 
 to differentiate rare disorders and are widely used by clinicians in clinic
 s. However, the current NGP approaches for rare disorders still have limita
 tions on two aspects: current approaches do not support ultra-rare and nove
 l disorders and no publicly available dataset. Therefore, we proposed Gesta
 ltMatcher and GestaltMatcher Database (GMDB) to tackle the current difficul
 ties.\n\n\n\nWe first developed GestaltMatcher as an extension to DeepGesta
 lt to support ultra-rare and novel disorders. GestaltMatcher first encoded 
 the frontal image into a 320-dimensional Facial Phenotype Descriptor (FDP).
  We further formed a Clinical Face Phenotype Space by the FDPs and quantifi
 ed the facial syndromic similarities among the patients by calculating the 
 cosine distance between two FDPs in the space. This approach can support ul
 tra-rare disorders and novel diseases and analyze the patients’ similaritie
 s to explore the novel gene-phenotype relationship.\n\n\n\nTo address the p
 roblem of lacking a public medical image dataset, we proposed GMDB, an open
 -access medical image database. GMDB hosts the medical images curated from 
 the publication and the consented patients, providing a valuable resource f
 or deep learning purposes and serving as reference material for clinician-s
 cientists to easily access and analyze medical images.\n\n\n\nIn the end, w
 e envision that GestaltMatcher and GMDB can be integrated into a diagnostic
  platform and further connected with patient match platforms such as MatchM
 aker Exchange to enable global collaboration and further improve the diagno
 sis of rare genetic disorders.
LOCATION:GMDS Xtra-Tracks 2024
DTSTAMP:20240522T065305Z
DTSTART:20240522T150000Z
DTEND:20240522T160000Z
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