This paper is in the following e-collection/theme issue:

New Methods (798) Imaging Informatics (157) Machine Learning (1190) Digital Biomarkers and Digital Phenotyping (155)
Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/19263, first published .
Efficiency of Computer-Aided Facial Phenotyping (DeepGestalt) in Individuals With and Without a Genetic Syndrome: Diagnostic Accuracy Study

Efficiency of Computer-Aided Facial Phenotyping (DeepGestalt) in Individuals With and Without a Genetic Syndrome: Diagnostic Accuracy Study

Efficiency of Computer-Aided Facial Phenotyping (DeepGestalt) in Individuals With and Without a Genetic Syndrome: Diagnostic Accuracy Study

Authors of this article:

Jean Tori Pantel 1, 2 Author Orcid Image ;   Nurulhuda Hajjir 1, 3 Author Orcid Image ;   Magdalena Danyel 1, 4 Author Orcid Image ;   Jonas Elsner 1 Author Orcid Image ;   Angela Teresa Abad-Perez 1 Author Orcid Image ;   Peter Hansen 1, 5 Author Orcid Image ;   Stefan Mundlos 1, 6 Author Orcid Image ;   Malte Spielmann 6, 7 Author Orcid Image ;   Denise Horn 1 Author Orcid Image ;   Claus-Eric Ott 1 Author Orcid Image ;   Martin Atta Mensah 1, 8 Author Orcid Image
Article Authors Cited by (19) Tweetations (10) Metrics

Journals

  1. Dunkel L, Fernandez-Luque L, Loche S, Savage M. Digital technologies to improve the precision of paediatric growth disorder diagnosis and management. Growth Hormone & IGF Research 2021;59:101408 View
  2. Hong D, Zheng Y, Xin Y, Sun L, Yang H, Lin M, Liu C, Li B, Zhang Z, Zhuang J, Qian M, Wang S. Genetic syndromes screening by facial recognition technology: VGG-16 screening model construction and evaluation. Orphanet Journal of Rare Diseases 2021;16(1) View
  3. Katsanis S, Claes P, Doerr M, Cook-Deegan R, Tenenbaum J, Evans B, Lee M, Anderton J, Weinberg S, Wagner J, Sane R. A survey of U.S. public perspectives on facial recognition technology and facial imaging data practices in health and research contexts. PLOS ONE 2021;16(10):e0257923 View
  4. Attallah O. A deep learning-based diagnostic tool for identifying various diseases via facial images. DIGITAL HEALTH 2022;8:205520762211244 View
  5. Pascolini G, Gaudioso F, Baldi M, Alario D, Dituri F, Novelli A, Baban A. Facial clues to the photosensitive trichothiodystrophy phenotype in childhood. Journal of Human Genetics 2023;68(6):437 View
  6. D’Souza A, Ryan E, Sidransky E. Facial features of lysosomal storage disorders. Expert Review of Endocrinology & Metabolism 2022;17(6):467 View
  7. Hsieh T, Bar-Haim A, Moosa S, Ehmke N, Gripp K, Pantel J, Danyel M, Mensah M, Horn D, Rosnev S, Fleischer N, Bonini G, Hustinx A, Schmid A, Knaus A, Javanmardi B, Klinkhammer H, Lesmann H, Sivalingam S, Kamphans T, Meiswinkel W, Ebstein F, Krüger E, Küry S, Bézieau S, Schmidt A, Peters S, Engels H, Mangold E, Kreiß M, Cremer K, Perne C, Betz R, Bender T, Grundmann-Hauser K, Haack T, Wagner M, Brunet T, Bentzen H, Averdunk L, Coetzer K, Lyon G, Spielmann M, Schaaf C, Mundlos S, Nöthen M, Krawitz P. GestaltMatcher facilitates rare disease matching using facial phenotype descriptors. Nature Genetics 2022;54(3):349 View
  8. Mensah M, Ott C, Horn D, Pantel J. A machine learning-based screening tool for genetic syndromes in children. The Lancet Digital Health 2022;4(5):e295 View
  9. Mahdi S, Matthews H, Nauwelaers N, Vanneste M, Gong S, Bouritsas G, Baynam G, Hammond P, Spritz R, Klein O, Hallgrimsson B, Peeters H, Bronstein M, Claes P. Multi-Scale Part-Based Syndrome Classification of 3D Facial Images. IEEE Access 2022;10:23450 View
  10. Porras A, Rosenbaum K, Tor-Diez C, Summar M, Linguraru M. A machine learning-based screening tool for genetic syndromes in children – Authors' reply. The Lancet Digital Health 2022;4(5):e296 View
  11. Park S, Kim J, Song T, Jang D. Case Report: The success of face analysis technology in extremely rare genetic diseases in Korea: Tatton–Brown–Rahman syndrome and Say–Barber –Biesecker–Young–Simpson variant of ohdo syndrome. Frontiers in Genetics 2022;13 View
  12. Ciancia S, Goedegebuure W, Grootjen L, Hokken-Koelega A, Kerkhof G, van der Kaay D. Computer-aided facial analysis as a tool to identify patients with Silver–Russell syndrome and Prader–Willi syndrome. European Journal of Pediatrics 2023;182(6):2607 View
  13. Zhang H, Lu Y, Qiao Y, Song H, Zhu Y. Role of "facial diagnosis" objectification in tumor diagnosis and treatment. Cancer Insight 2022;1(1):62 View
  14. Yankee T, Oh S, Winchester E, Wilderman A, Robinson K, Gordon T, Rosenfeld J, VanOudenhove J, Scott D, Leslie E, Cotney J. Integrative analysis of transcriptome dynamics during human craniofacial development identifies candidate disease genes. Nature Communications 2023;14(1) View
  15. Lesmann H, Klinkhammer H, M. Krawitz P. The future role of facial image analysis in ACMG classification guidelines. Medizinische Genetik 2023;35(2):115 View
  16. Kim J, Ko H, Woo H, Kim W. Lessons Learned from the Point-of-Care Use of a Facial Analysis Technology. Annals of Child Neurology 2023;31(4):271 View
  17. Ferri-Rufete D, López-González A, Casas-Alba D, Cuadras D, Palau F, Martínez-Monseny A. Clinical Genetics Assessment Triangle (CGAT): A simple tool to identify patients with genetic conditions. European Journal of Medical Genetics 2023;66(11):104858 View
  18. Carrer A, Romaniello M, Calderara M, Mariani M, Biondi A, Selicorni A. Application of the Face2Gene tool in an Italian dysmorphological pediatric clinic: Retrospective validation and future perspectives. American Journal of Medical Genetics Part A 2023 View

Books/Policy Documents

  1. Hartsfield J, Morford L, Shafi A. Integrated Clinical Orthodontics. View