Federated vs Local vs Central Deep Learning of Tooth Segmentation on Panoramic Radiographs.

Journal: Journal of dentistry
Published Date: May 18, 2023

Abstract

OBJECTIVE: Federated Learning (FL) enables collaborative training of artificial intelligence (AI) models from multiple data sources without directly sharing data. Due to the large amount of sensitive data in dentistry, FL may be particularly relevant for oral and dental research and applications. This study, for the first time, employed FL for a dental task, automated tooth segmentation on panoramic radiographs.

Authors

  • Lisa Schneider
    Department of Simulation and Graphics, Otto von Guericke University Magdeburg, Germany.
  • Roman Rischke
    Department of Artificial Intelligence, Fraunhofer Heinrich Hertz Institute, Berlin, Germany.
  • Joachim Krois
    Department of Operative and Preventive Dentistry, Charité - Universitätsmedizin Berlin, Berlin, Germany.
  • Aleksander Krasowski
    Department of Oral Diagnostics, Digital Health and Health Services Research, Charité-Universitätsmedizin Berlin, Aßmannshauser Str. 4-6, 14197, Berlin, Germany.
  • Martha Büttner
    Charité - Universitätsmedizin Berlin, Berlin, Germany. bdjmanuscripts@nature.com.
  • Hossein Mohammad-Rahimi
    Division of Artificial Intelligence Imaging Research, University of Maryland School of Dentistry, Baltimore, MD 21201, USA.
  • Akhilanand Chaurasia
    Department of Oral Medicine and Radiology, King George's Medical University, Lucknow, India.
  • Nielsen S Pereira
    ITU/WHO Focus Group on AI for Health, Topic Group Dental Diagnostics and Digital Dentistry, Geneva, Switzerland; Private Practice in Oral and Maxillofacial Radiology, Rio de Janeiro, Brazil.
  • Jae-Hong Lee
    Department of Neurology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea.
  • Sergio E Uribe
    Topic Group Dental Diagnostics and Digital Dentistry, ITU/WHO Focus Group AI on Health, Berlin, Germany; Department of Conservative Dentistry and Oral Health & Bioinformatics Research Unit, Riga Stradins University, Riga, Latvia; School of Dentistry, Universidad Austral de Chile, Valdivia, Chile; Baltic Biomaterials Centre of Excellence, Headquarters at Riga Technical University, Riga, Latvia.
  • Shahriar Shahab
    ITU/WHO Focus Group on AI for Health, Topic Group Dental Diagnostics and Digital Dentistry, Geneva, Switzerland; Department of Oral and Maxillofacial Radiology, School of Dentistry, Shahed University of Medical Sciences, Tehran, Iran.
  • Revan Birke Koca-Ünsal
    ITU/WHO Focus Group on AI for Health, Topic Group Dental Diagnostics and Digital Dentistry, Geneva, Switzerland; Department of Periodontology, Faculty of Dentistry, University of Kyrenia, Kyrenia, Cyprus.
  • Gürkan Ünsal
    Faculty of Dentistry, Department of Dentomaxillofacial Radiology, Near East University, Nicosia, Cyprus.
  • Yolanda Martinez-Beneyto
    Department of Preventive Dentistry, University of Murcia, Murcia, Spain.
  • Janet Brinz
    ITU/WHO Focus Group on AI for Health, Topic Group Dental Diagnostics and Digital Dentistry, Geneva, Switzerland; Department of Conservative Dentistry and Periodontology, University Hospital, LMU Munich, Munich, Germany.
  • Olga Tryfonos
    ITU/WHO Focus Group AI on Health, Topic Group Dentistry, Switzerland; Department of Periodontology and Oral Biochemistry, Academic Centre for Dentistry Amsterdam (ACTA), University of Amsterdam and Vrije Universiteit Amsterdam, Amsterdam, The Netherland.
  • Falk Schwendicke
    Department of Operative and Preventive Dentistry, Charité - Universitätsmedizin Berlin, Berlin, Germany. falk.schwendicke@charite.de.

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