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  • © 2021

Machine Learning in Dentistry

Editors:

(view affiliations)
  • Reviews use of machine learning in contemporary dentistry

  • Covers applications in dental practice and research

  • Highlights benefits, opportunities, and challenges

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  • ISBN: 978-3-030-71881-7
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Table of contents (14 chapters)

  1. Front Matter

    Pages i-x
  2. Machine Learning for Dental Imaging

    1. Front Matter

      Pages 1-1
    2. Machine Learning for CBCT Segmentation of Craniomaxillofacial Bony Structures

      • Chunfeng Lian, James J. Xia, Dinggang Shen, Li Wang
      Pages 3-13
    3. Machine Learning for Craniomaxillofacial Landmark Digitization of 3D Imaging

      • Jun Zhang, Mingxia Liu, Li Wang, Chunfeng Lian, Dinggang Shen
      Pages 15-26
    4. Segmenting Bones from Brain MRI via Generative Adversarial Learning

      • Xu Chen, Chunfeng Lian, Li Wang, Pew-Thian Yap, James J. Xia, Dinggang Shen
      Pages 27-40
    5. Sparse Dictionary Learning for 3D Craniomaxillofacial Skeleton Estimation Based on 2D Face Photographs

      • Deqiang Xiao, Chunfeng Lian, Li Wang, Hannah Deng, Kim-Han Thung, Pew-Thian Yap et al.
      Pages 41-53
    6. Machine Learning for Facial Recognition in Orthodontics

      • Chihiro Tanikawa, Lee Chonho
      Pages 55-65
  3. Machine Learning for Oral Diagnosis and Treatment

    1. Front Matter

      Pages 67-67
    2. Machine/Deep Learning for Performing Orthodontic Diagnoses and Treatment Planning

      • Chihiro Tanikawa, Tomoyuki Kajiwara, Yuujin Shimizu, Takashi Yamashiro, Chenhui Chu, Hajime Nagahara
      Pages 69-78
    3. Machine Learning in Orthodontics: A New Approach to the Extraction Decision

      • Mary Lanier Zaytoun Berne, Feng-Chang Lin, Yi Li, Tai-Hsien Wu, Esther Chien, Ching-Chang Ko
      Pages 79-90
    4. Machine (Deep) Learning for Characterization of Craniofacial Variations

      • Si Chen, Te-Ju Wu, Tai-Hsien Wu, Matthew Pastewait, Anna Zheng, Li Wang et al.
      Pages 91-104
    5. Patient-Specific Reference Model for Planning Orthognathic Surgery

      • Hannah H. Deng, Li Wang, Yi Ren, Jaime Gateno, Zhen Tang, Ken-Chung Chen et al.
      Pages 105-114
  4. Machine Learning and Dental Designs

    1. Front Matter

      Pages 115-115
    2. Machine (Deep) Learning for Orthodontic CAD/CAM Technologies

      • Tai-Hsien Wu, Chunfeng Lian, Christian Piers, Matthew Pastewait, Li Wang, Dinggang Shen et al.
      Pages 117-129
    3. Assessment of Outcomes by Using Machine Learning

      • Shankar Rengasamy Venugopalan, Mohammed H. Elnagar, Deepti S. Karhade, Veerasathpurush Allareddy
      Pages 131-143
  5. Machine Learning Supporting Dental Research

    1. Front Matter

      Pages 145-145
    2. Machine Learning in Evidence Synthesis Research

      • Alonso Carrasco-Labra, Olivia Urquhart, Heiko Spallek
      Pages 147-161
    3. Machine Learning and Deep Learning in Genetics and Genomics

      • Di Wu, Deepti S. Karhade, Malvika Pillai, Min-Zhi Jiang, Le Huang, Gang Li et al.
      Pages 163-181
    4. Machine (Deep) Learning and Finite Element Modeling

      • Yan-Ting Lee, Tai-Hsien Wu, Mei-Ling Lin, Ching-Chang Ko
      Pages 183-188

About this book

This book reviews all aspects of the use of machine learning in contemporary dentistry, clearly explaining its significance for dental imaging, oral diagnosis and treatment, dental designs, and dental research. Machine learning is an emerging field of artificial intelligence research and practice in which computer agents are employed to improve perception, cognition, and action based on their ability to “learn”, for example through use of big data techniques. Its application within dentistry is designed to promote personalized and precision patient care, with enhancement of diagnosis and treatment planning. In this book, readers will find up-to-date information on different machine learning tools and their applicability in various dental specialties. The selected examples amply illustrate the opportunities to employ a machine learning approach within dentistry while also serving to highlight the associated challenges. Machine Learning in Dentistry will be of value for all dental practitioners and researchers who wish to learn more about the potential benefits of using machine learning techniques in their work.

Keywords

  • Dental big data
  • Digital oral imaging
  • Digital Dentistry
  • Oral diagnosis
  • Artificial intelligence in dentistry

Editors and Affiliations

  • Ohio State University, Columbus, USA

    Ching-Chang Ko

  • School of Biomedical Engineering, ShanghaiTech University, Shanghai, China

    Dinggang Shen

  • University of North Carolina at Chapel Hill, Chapel Hill, USA

    Li Wang

About the editors

Ching-Chang Ko, DDS, MS, PhD, is Professor and Vig/William Endowed Chair of the Division of Orthodontics, College of Dentistry at the Ohio State University, Columbus, OH, USA. Dr. Ko graduated in Dentistry from Kaohsiung Medical College in Taiwan in 1984 and subsequently gained his MS in Bioengineering at National Yang-Ming University and his PhD in Bioengineering and Biomaterials at the University of Michigan. He completed his Certificate in Orthodontics at the University of Minnesota in 2006. He has been Professor of Orthodontics at University of North Carolina 2006-2019 and served as Program Director since 2013 and Chair 2017-2019. He moved to Ohio State University in 2020 to join the Division of Orthodontics and the Translational Data Analytics Institute to develop Artificial Intelligence in Orthodontics.  In 2017 Dr. Ko was also a Guest Professor at Peking University in China. Dr. Ko is the author of 150 peer-reviewed journal articles. He is an Associate Editor for The Angle Orthodontist and an editorial board member of the Chinese Journal of Orthodontics and acts as a reviewer for numerous journals. NIH, NSF, Whitaker Foundation, NC Biotech, and companies (e.g., 3M /ESPE, Smartee Inc., N2Bio) have supported his research, in part. He is Member of International Association for Dental Research (IADR) and American Association of Orthodontists (AAO).

Dinggang Shen, PhD, FIEEE, FAIMBE, FIAPR, is Professor and Dean of School of Biomedical Engineering, ShanghaiTech University, and also Co-CEO of United Imaging Intelligence (UII). He is Fellow of IEEE, Fellow of The American Institute for Medical and Biological Engineering (AIMBE), Fellow of The International Association for Pattern Recognition (IAPR), and also Fellow of The Medical Image Computing and Computer Assisted Intervention (MICCAI) Society. He was Jeffrey Houpt Distinguished Investigator, and (Tenured) Full Professor in the University of North Carolina at Chapel Hill (UNC-CH). His research interests include medical image analysis, computer vision, and pattern recognition. He has published more than 1100 peer-reviewed papers in the international journals and conference proceedings, with H-index 107. He serves as an editorial board member for eight international journals. Also, he has served in the Board of Directors, The Medical Image Computing and Computer Assisted Intervention (MICCAI) Society, in 2012-2015, and was General Chair for MICCAI 2019.

Li Wang, BS, PhD, is an Assistant Professor in the Department of Radiology and Biomedical Research Imaging Center at the University of North Carolina at Chapel Hill. He is the director of Developing Brain Computing Lab. He joined the University of North Carolina at Chapel Hill as a postdoctoral research fellow in 2010, after gaining his doctorate in Pattern Recognition and Intelligent Systems from Nanjing University of Science and Technology, and took up his present post in 2015. Dr. Wang’s research focuses on the development of innovative computational methods and tools for processing and analyzing medical imaging data. Among his achievements are the creation of a comprehensive set of advanced CBCT-dedicated tools for processing CBCTs for patients with craniomaxillofacial deformities. He has been lead or co-author of 76 articles in peer-reviewed journals and has an H-index of 40. He acts as a reviewer for numerous journals. He is a senior member of the IEEE..


Bibliographic Information

  • Book Title: Machine Learning in Dentistry

  • Editors: Ching-Chang Ko, Dinggang Shen, Li Wang

  • DOI: https://doi.org/10.1007/978-3-030-71881-7

  • Publisher: Springer Cham

  • eBook Packages: Medicine, Medicine (R0)

  • Copyright Information: Springer Nature Switzerland AG 2021

  • Hardcover ISBN: 978-3-030-71880-0

  • eBook ISBN: 978-3-030-71881-7

  • Edition Number: 1

  • Number of Pages: X, 188

  • Number of Illustrations: 14 b/w illustrations, 76 illustrations in colour

  • Topics: Dentistry, Big Data

Buying options

eBook
USD 119.00
Price excludes VAT (USA)
  • ISBN: 978-3-030-71881-7
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
Softcover Book
USD 109.99
Price excludes VAT (USA)
This title has not yet been released. You will be able to pre-order it soon.
Hardcover Book
USD 159.99
Price excludes VAT (USA)