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

New Methods (1030) Digital Biomarkers and Digital Phenotyping (227) Imaging Informatics (235) Machine Learning (1730) Data Science (305) Artificial Intelligence (1721)

Published on in Vol 26 (2024)

This is a member publication of Charite - Universitaetsmedizin Berlin, Medizinische Bibliothek, Germany

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/42904, first published .
Validation of 3 Computer-Aided Facial Phenotyping Tools (DeepGestalt, GestaltMatcher, and D-Score): Comparative Diagnostic Accuracy Study

Validation of 3 Computer-Aided Facial Phenotyping Tools (DeepGestalt, GestaltMatcher, and D-Score): Comparative Diagnostic Accuracy Study

Validation of 3 Computer-Aided Facial Phenotyping Tools (DeepGestalt, GestaltMatcher, and D-Score): Comparative Diagnostic Accuracy Study

Authors of this article:

Alisa Maria Vittoria Reiter1 Author Orcid Image ;   Jean Tori Pantel1, 2, 3 Author Orcid Image ;   Magdalena Danyel1, 4, 5 Author Orcid Image ;   Denise Horn1 Author Orcid Image ;   Claus-Eric Ott1 Author Orcid Image ;   Martin Atta Mensah1, 6 Author Orcid Image
Article Authors Cited by (5) Tweetations (13) Metrics

Journals

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  2. Nishimura S, Ma C, Sidransky E, Ryan E. Obstacles to Early Diagnosis of Gaucher Disease. Therapeutics and Clinical Risk Management 2025;Volume 21:93 View
  3. Pascolini G, Didona D, Tarani L. An Artificial Intelligence Approach to the Craniofacial Recapitulation of Crisponi/Cold-Induced Sweating Syndrome 1 (CISS1/CISS) from Newborns to Adolescent Patients. Diagnostics 2025;15(5):521 View
  4. Guleria A, Krishan K, Sharma V. Assessment of facial and nasal phenotypes: Implications in forensic facial reconstruction. Archives of Biological Sciences 2025;77(1):61 View

Books/Policy Documents

  1. Crimi M. The Digital Doctor. View