Deep learning: A primer for dentists and dental researchers

被引:40
作者
Mohammad-Rahimi, Hossein [1 ]
Rokhshad, Rata [2 ]
Bencharit, Sompop [3 ,4 ]
Krois, Joachim [1 ]
Schwendicke, Falk [1 ,5 ]
机构
[1] WHO Focus Grp AI Hlth, ITU, Top Grp Dent Diagnost & Digital Dent, Berlin, Germany
[2] Boston Univ, Med Ctr, Dept Med, Sect Endocrinol Nutr & Diabet,Vitamin D, Boston, MA USA
[3] Virginia Commonwealth Univ, Philips Inst Oral Hlth Res, Coll Engn, Sch Dent,Dept Oral & Craniofacial Mol Biol, Richmond, VA 23298 USA
[4] Virginia Commonwealth Univ, Coll Engn, Dept Biomed Engn, Richmond, VA 23298 USA
[5] Charite Univ Med Berlin, Dept Oral Diagnost Digital Hlth & Hlth Serv Res, Assmannshauser Str 4-6, D-14197 Berlin, Germany
关键词
Artificial intelligence; Deep learning; Neural networks; Dentistry; ARTIFICIAL-INTELLIGENCE; BENCHMARKING; RADIOLOGY; DATABASE; DATASET; PRIVACY;
D O I
10.1016/j.jdent.2023.104430
中图分类号
R78 [口腔科学];
学科分类号
1003 ;
摘要
Objectives: Despite deep learning's wide adoption in dental artificial intelligence (AI) research, researchers from other dental fields and, more so, dental professionals may find it challenging to understand and interpret deep learning studies, their employed methods, and outcomes. The objective of this primer is to explain the basic concept of deep learning. It will lay out the commonly used terms, and describe different deep learning ap-proaches, their methods, and outcomes.Methods: Our research is based on the latest review studies, medical primers, as well as the state-of-the-art research on AI and deep learning, which have been gathered in the current study.Results: In this study, a basic understanding of deep learning models and various approaches to deep learning is presented. An overview of data management strategies for deep learning projects is presented, including data collection, data curation, data annotation, and data preprocessing. Additionally, we provided a step-by-step guide for completing a real-world project.Conclusion: Researchers and clinicians can benefit from this study by gaining insight into deep learning. It can be used to critically appraise existing work or plan new deep learning projects.Clinical significance: This study may be useful to dental researchers and professionals who are assessing and appraising deep learning studies within the field of dentistry.
引用
收藏
页数:12
相关论文
共 50 条
  • [1] Deep learning for cardiovascular medicine: a practical primer
    Krittanawong, Chayakrit
    Johnson, Kipp W.
    Rosenson, Robert S.
    Wang, Zhen
    Aydar, Mehmet
    Baber, Usman
    Min, James K.
    Tang, W. H. Wilson
    Halperin, Jonathan L.
    Narayan, Sanjiv M.
    EUROPEAN HEART JOURNAL, 2019, 40 (25) : 2058 - +
  • [2] Deep Learning: A Primer for Psychologists
    Urban, Christopher J.
    Gates, Kathleen M.
    PSYCHOLOGICAL METHODS, 2021, 26 (06) : 743 - 773
  • [3] Machine learning and artificial intelligence in neuroscience: A primer for researchers
    Badrulhisham, Fakhirah
    Pogatzki-Zahn, Esther
    Segelcke, Daniel
    Spisak, Tamas
    Vollert, Jan
    BRAIN BEHAVIOR AND IMMUNITY, 2024, 115 : 470 - 479
  • [4] A robust deep learning model for the classification of dental implant brands
    Kurtulus, Ikbal Leblebicioglu
    Lubbad, Mohammed
    Yilmaz, Ozden Melis Durmaz
    Kilic, Kerem
    Karaboga, Dervis
    Basturk, Alper
    Akay, Bahriye
    Nalbantoglu, Ufuk
    Yilmaz, Serkan
    Ayata, Mustafa
    Pacal, Ishak
    JOURNAL OF STOMATOLOGY ORAL AND MAXILLOFACIAL SURGERY, 2024, 125 (05)
  • [5] Predicting sequenced dental treatment plans from electronic dental records using deep learning
    Chen, Haifan
    Liu, Pufan
    Chen, Zhaoxing
    Chen, Qingxiao
    Wen, Zaiwen
    Xie, Ziqing
    ARTIFICIAL INTELLIGENCE IN MEDICINE, 2024, 147
  • [6] Deep learning methods for fully automated dental age estimation on orthopantomograms
    Shi, Yuchao
    Ye, Zelin
    Guo, Jixiang
    Tang, Yueting
    Dong, Wenxuan
    Dai, Jiaqi
    Miao, Yu
    You, Meng
    CLINICAL ORAL INVESTIGATIONS, 2024, 28 (03)
  • [7] Deep-learning approach for caries detection and segmentation on dental bitewing radiographs
    Bayrakdar, Ibrahim Sevki
    Orhan, Kaan
    Akarsu, Serdar
    Celik, Ozer
    Atasoy, Samet
    Pekince, Adem
    Yasa, Yasin
    Bilgir, Elif
    Saglam, Hande
    Aslan, Ahmet Faruk
    Odabas, Alper
    ORAL RADIOLOGY, 2022, 38 (04) : 468 - 479
  • [8] Deep-learning approach for caries detection and segmentation on dental bitewing radiographs
    Ibrahim Sevki Bayrakdar
    Kaan Orhan
    Serdar Akarsu
    Özer Çelik
    Samet Atasoy
    Adem Pekince
    Yasin Yasa
    Elif Bilgir
    Hande Sağlam
    Ahmet Faruk Aslan
    Alper Odabaş
    Oral Radiology, 2022, 38 : 468 - 479
  • [9] Clinical Validation of Deep Learning for Segmentation of Multiple Dental Features in Periapical Radiographs
    Jagtap, Rohan
    Samata, Yalamanchili
    Parekh, Amisha
    Tretto, Pedro
    Roach, Michael D.
    Sethumanjusha, Saranu
    Tejaswi, Chennupati
    Jaju, Prashant
    Friedel, Alan
    Briner Garrido, Michelle
    Feinberg, Maxine
    Suri, Mini
    BIOENGINEERING-BASEL, 2024, 11 (10):
  • [10] Automatic dental biofilm detection based on deep learning
    Andrade, Katia Montanha
    Silva, Bernardo Peters Menezes
    de Oliveira, Luciano Reboucas
    Cury, Patricia Ramos
    JOURNAL OF CLINICAL PERIODONTOLOGY, 2023, 50 (05) : 571 - 581