Identifying facial phenotypes of genetic disorders using deep learning

We present a facial image analysis framework, DeepGestalt, using computer vision and deep-learning algorithms, that quantifies similarities to hundreds of syndromes

Yaron Gurovich; Yair Hanani; Omri Bar; Guy Nadav; Nicole Fleischer; Dekel Gelbman; Lina Basel-Salmon; Peter M. Krawitz; Susanne B. Kamphausen; Martin Zenker; Lynne M. Bird; Karen W. Gripp

2018

Scholarcy highlights

  • Syndromic genetic conditions, in aggregate, affect 8% of the population1
  • Published data are available from the reported references and in Supplementary Table 6
  • Restricted data are curated from Face2Gene users under a license and cannot be published, to protect patient privacy
  • In aggregate, affect 8% of the population1
  • The authors thank the patients and their families, as well as Face2Gene users worldwide who contribute with their knowledge and dedication to the improvement of this and other tools for the ultimate benefit of better healthcare
  • Y.G., Y.H., O.B., G.N., N.F. and D.G. are employees of FDNA; L.B.-S., P.M.K. and K.W.G. are advisors of FDNA; L.B.-S., P.M.K., M.Z., L.M.B. and K.W.G. are members of the scientific advisory board of FDNA

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