Literature DB >> 31950592

Radiomics and Machine Learning in Oral Healthcare.

André Ferreira Leite1,2, Karla de Faria Vasconcelos2, Holger Willems3, Reinhilde Jacobs2,4.   

Abstract

The increasing storage of information, data, and forms of knowledge has led to the development of new technologies that can help to accomplish complex tasks in different areas, such as in dentistry. In this context, the role of computational methods, such as radiomics and Artificial Intelligence (AI) applications, has been progressing remarkably for dentomaxillofacial radiology (DMFR). These tools bring new perspectives for diagnosis, classification, and prediction of oral diseases, treatment planning, and for the evaluation and prediction of outcomes, minimizing the possibilities of human errors. A comprehensive review of the state-of-the-art of using radiomics and machine learning (ML) for imaging in oral healthcare is presented in this paper. Although the number of published studies is still relatively low, the preliminary results are very promising and in a near future, an augmented dentomaxillofacial radiology (ADMFR) will combine the use of radiomics-based and AI-based analyses with the radiologist's evaluation. In addition to the opportunities and possibilities, some challenges and limitations have also been discussed for further investigations.
© 2020 The Authors. Proteomics - Clinical Applications published by WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Entities:  

Keywords:  CBCT; machine learning; oral health; radiomics

Mesh:

Year:  2020        PMID: 31950592     DOI: 10.1002/prca.201900040

Source DB:  PubMed          Journal:  Proteomics Clin Appl        ISSN: 1862-8346            Impact factor:   3.494


  10 in total

1.  Artificial intelligence-driven novel tool for tooth detection and segmentation on panoramic radiographs.

Authors:  André Ferreira Leite; Adriaan Van Gerven; Holger Willems; Thomas Beznik; Pierre Lahoud; Hugo Gaêta-Araujo; Myrthel Vranckx; Reinhilde Jacobs
Journal:  Clin Oral Investig       Date:  2020-08-26       Impact factor: 3.573

Review 2.  Potential and impact of artificial intelligence algorithms in dento-maxillofacial radiology.

Authors:  Kuo Feng Hung; Qi Yong H Ai; Yiu Yan Leung; Andy Wai Kan Yeung
Journal:  Clin Oral Investig       Date:  2022-04-19       Impact factor: 3.606

3.  Artificial Intelligence and Radiomics in Head and Neck Cancer Care: Opportunities, Mechanics, and Challenges.

Authors:  Lisanne V van Dijk; Clifton D Fuller
Journal:  Am Soc Clin Oncol Educ Book       Date:  2021-03

4.  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

5.  Differential diagnosis of ameloblastoma and odontogenic keratocyst by machine learning of panoramic radiographs.

Authors:  Zijia Liu; Jiannan Liu; Guangtao Zhai; Jing Han; Zijie Zhou; Qiaoyu Zhang; Hao Wu
Journal:  Int J Comput Assist Radiol Surg       Date:  2021-02-06       Impact factor: 2.924

6.  Salivary cystatin S levels in children with early childhood caries in comparison with caries-free children; statistical analysis and machine learning.

Authors:  Maryam Koopaie; Mahsa Salamati; Roshanak Montazeri; Mansour Davoudi; Sajad Kolahdooz
Journal:  BMC Oral Health       Date:  2021-12-18       Impact factor: 2.757

7.  Artificial Intelligence: A New Diagnostic Software in Dentistry: A Preliminary Performance Diagnostic Study.

Authors:  Francesca De Angelis; Nicola Pranno; Alessio Franchina; Stefano Di Carlo; Edoardo Brauner; Agnese Ferri; Gerardo Pellegrino; Emma Grecchi; Funda Goker; Luigi Vito Stefanelli
Journal:  Int J Environ Res Public Health       Date:  2022-02-02       Impact factor: 3.390

Review 8.  Scope and challenges of machine learning-based diagnosis and prognosis in clinical dentistry: A literature review.

Authors:  Lilian Toledo Reyes; Jessica Klöckner Knorst; Fernanda Ruffo Ortiz; Thiago Machado Ardenghi
Journal:  J Clin Transl Res       Date:  2021-07-30

9.  A Fused Deep Learning Architecture for the Detection of the Relationship between the Mandibular Third Molar and the Mandibular Canal.

Authors:  Cansu Buyuk; Nurullah Akkaya; Belde Arsan; Gurkan Unsal; Secil Aksoy; Kaan Orhan
Journal:  Diagnostics (Basel)       Date:  2022-08-20

10.  Artificial Intelligence (AI)-Driven Molar Angulation Measurements to Predict Third Molar Eruption on Panoramic Radiographs.

Authors:  Myrthel Vranckx; Adriaan Van Gerven; Holger Willems; Arne Vandemeulebroucke; André Ferreira Leite; Constantinus Politis; Reinhilde Jacobs
Journal:  Int J Environ Res Public Health       Date:  2020-05-25       Impact factor: 3.390

  10 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.