Literature DB >> 32803680

A brief introduction to concepts and applications of artificial intelligence in dental imaging.

Ruben Pauwels1.   

Abstract

This report aims to summarize the fundamental concepts of Artificial Intelligence (AI), and to provide a non-exhaustive overview of AI applications in dental imaging, comprising diagnostics, forensics, image processing and image reconstruction. AI has arguably become the hottest topic in radiology in recent years owing to the increased computational power available to researchers, the continuing collection of digital data, as well as the development of highly efficient algorithms for machine learning and deep learning. It is now feasible to develop highly robust AI applications that make use of the vast amount of data available to us, and that keep learning and improving over time.

Keywords:  Artificial intelligence; Deep learning; Dentistry; Machine learning; Radiology

Mesh:

Year:  2020        PMID: 32803680     DOI: 10.1007/s11282-020-00468-5

Source DB:  PubMed          Journal:  Oral Radiol        ISSN: 0911-6028            Impact factor:   1.852


  25 in total

1.  Deep Learning at Chest Radiography: Automated Classification of Pulmonary Tuberculosis by Using Convolutional Neural Networks.

Authors:  Paras Lakhani; Baskaran Sundaram
Journal:  Radiology       Date:  2017-04-24       Impact factor: 11.105

Review 2.  Implementing Machine Learning in Radiology Practice and Research.

Authors:  Marc Kohli; Luciano M Prevedello; Ross W Filice; J Raymond Geis
Journal:  AJR Am J Roentgenol       Date:  2017-01-26       Impact factor: 3.959

3.  Automated Critical Test Findings Identification and Online Notification System Using Artificial Intelligence in Imaging.

Authors:  Luciano M Prevedello; Barbaros S Erdal; John L Ryu; Kevin J Little; Mutlu Demirer; Songyue Qian; Richard D White
Journal:  Radiology       Date:  2017-07-03       Impact factor: 11.105

4.  Artificial intelligence for breast cancer screening: Opportunity or hype?

Authors:  Nehmat Houssami; Christoph I Lee; Diana S M Buist; Dacheng Tao
Journal:  Breast       Date:  2017-09-20       Impact factor: 4.380

5.  Artificial Intelligence: Threat or Boon to Radiologists?

Authors:  Michael Recht; R Nick Bryan
Journal:  J Am Coll Radiol       Date:  2017-08-19       Impact factor: 5.532

6.  Human and machine learning in non-Markovian decision making.

Authors:  Aaron Michael Clarke; Johannes Friedrich; Elisa M Tartaglia; Silvia Marchesotti; Walter Senn; Michael H Herzog
Journal:  PLoS One       Date:  2015-04-21       Impact factor: 3.240

7.  Artificial intelligence for analyzing orthopedic trauma radiographs.

Authors:  Jakub Olczak; Niklas Fahlberg; Atsuto Maki; Ali Sharif Razavian; Anthony Jilert; André Stark; Olof Sköldenberg; Max Gordon
Journal:  Acta Orthop       Date:  2017-07-06       Impact factor: 3.717

8.  Pixel-Level Deep Segmentation: Artificial Intelligence Quantifies Muscle on Computed Tomography for Body Morphometric Analysis.

Authors:  Hyunkwang Lee; Fabian M Troschel; Shahein Tajmir; Georg Fuchs; Julia Mario; Florian J Fintelmann; Synho Do
Journal:  J Digit Imaging       Date:  2017-08       Impact factor: 4.056

Review 9.  An overview of deep learning in the field of dentistry.

Authors:  Jae-Joon Hwang; Yun-Hoa Jung; Bong-Hae Cho; Min-Suk Heo
Journal:  Imaging Sci Dent       Date:  2019-03-25

10.  Deep learning for neuroimaging: a validation study.

Authors:  Sergey M Plis; Devon R Hjelm; Ruslan Salakhutdinov; Elena A Allen; Henry J Bockholt; Jeffrey D Long; Hans J Johnson; Jane S Paulsen; Jessica A Turner; Vince D Calhoun
Journal:  Front Neurosci       Date:  2014-08-20       Impact factor: 4.677

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  5 in total

Review 1.  Radiographic modalities for diagnosis of caries in a historical perspective: from film to machine-intelligence supported systems.

Authors:  Ann Wenzel
Journal:  Dentomaxillofac Radiol       Date:  2021-03-04       Impact factor: 3.525

2.  Attitude of Brazilian dentists and dental students regarding the future role of artificial intelligence in oral radiology: a multicenter survey.

Authors:  Ruben Pauwels; Yumi Chokyu Del Rey
Journal:  Dentomaxillofac Radiol       Date:  2021-01-12       Impact factor: 3.525

3.  A dose-neutral image quality comparison of different CBCT and CT systems using paranasal sinus imaging protocols and phantoms.

Authors:  Ari-Petteri Ronkainen; Ali Al-Gburi; Timo Liimatainen; Hanna Matikka
Journal:  Eur Arch Otorhinolaryngol       Date:  2022-01-27       Impact factor: 3.236

4.  Artificial intelligence-based diagnostics of molar-incisor-hypomineralization (MIH) on intraoral photographs.

Authors:  Jule Schönewolf; Ole Meyer; Paula Engels; Anne Schlickenrieder; Reinhard Hickel; Volker Gruhn; Marc Hesenius; Jan Kühnisch
Journal:  Clin Oral Investig       Date:  2022-05-24       Impact factor: 3.606

Review 5.  Dental Caries Diagnosis and Detection Using Neural Networks: A Systematic Review.

Authors:  María Prados-Privado; Javier García Villalón; Carlos Hugo Martínez-Martínez; Carlos Ivorra; Juan Carlos Prados-Frutos
Journal:  J Clin Med       Date:  2020-11-06       Impact factor: 4.241

  5 in total

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