Deep learning predicted perceived age is a reliable approach for analysis of facial ageing: A proof of principle study.

Journal: Journal of the European Academy of Dermatology and Venereology : JEADV
Published Date: Oct 3, 2024

Abstract

BACKGROUND: Perceived age (PA) has been associated with mortality, genetic variants linked to ageing and several age-related morbidities. However, estimating PA in large datasets is laborious and costly to generate, limiting its practical applicability.

Authors

  • Conor Turner
    Interdisciplinary Computing and Complex BioSystems (ICOS) Research Group, School of Computing, Newcastle University, Newcastle upon Tyne, UK.
  • Luba M Pardo
    Department of Dermatology, Erasmus MC University Medical Center Rotterdam, Rotterdam, The Netherlands.
  • David A Gunn
    Unilever King's Biosciences Innovation Hub, King's College London, London, UK.
  • Ruediger Zillmer
    Unilever Research and Development, Port Sunlight, UK.
  • Selma Mekić
    Department of Dermatology, University Medical Center Utrecht, Utrecht, The Netherlands.
  • Fan Liu
    Hunan Provincial Key Laboratory of Dong Medicine, Hunan University of Medicine, Huaihua, China.
  • M Arfan Ikram
  • Caroline C W Klaver
    Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Center, Nijmegen, The Netherlands; Department of Ophthalmology, Erasmus University Medical Center, Rotterdam, The Netherlands; Department of Epidemiology, Erasmus University Medical Center, Rotterdam, The Netherlands; Department of Ophthalmology, Radboud University Medical Center, Nijmegen, The Netherlands; Institute for Molecular and Clinical Ophthalmology, Basel, Switzerland.
  • Pauline H Croll
    Department of Epidemiology, Erasmus MC University Medical Center Rotterdam, Rotterdam, The Netherlands.
  • André Goedegebure
    Department of Otorhinolaryngology, Head and Neck Surgery, Erasmus MC University Medical Center Rotterdam, Rotterdam, The Netherlands.
  • Katerina Trajanoska
    Department of Epidemiology, Erasmus MC University Medical Center Rotterdam, Rotterdam, The Netherlands.
  • Fernando Rivadeneira
    Department of Internal Medicine, Erasmus University Medical Center, Rotterdam, Netherlands.
  • Maryam Kavousi
    Department of Epidemiology, Erasmus MC, Rotterdam, the Netherlands.
  • Guy G O Brusselle
    Department of Epidemiology, Erasmus MC University Medical Center Rotterdam, Rotterdam, The Netherlands.
  • Manfred Kayser
    Department of Genetic Identification, Erasmus MC University Medical Center Rotterdam, Rotterdam, the Netherlands. Electronic address: m.kayser@erasmusmc.nl.
  • Tamar Nijsten
    Department of Dermatology, Erasmus MC Cancer Institute, University Medical Center Rotterdam, Rotterdam, 3015 GD, The Netherlands.
  • Jaume Bacardit

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