Evaluation of deep learning and convolutional neural network algorithms for mandibular fracture detection using radiographic images: A systematic review and meta-analysis-Reference-Cited by-同舟云学术

Evaluation of deep learning and convolutional neural network algorithms for mandibular fracture detection using radiographic images: A systematic review and meta-analysis

Published:2024 Issue: Volume:54 Page:
ISSN:2233-7822
Container-title:Imaging Science in Dentistry
language:en
Short-container-title:Imaging Sci Dent

Author:

Dashti Mahmood¹^ORCID,Ghaedsharaf Sahar²^ORCID,Ghasemi Shohreh³^ORCID,Zare Niusha⁴^ORCID,Constantin Elena-Florentina⁵^ORCID,Fahimipour Amir⁶^ORCID,Tajbakhsh Neda⁷^ORCID,Ghadimi Niloofar⁸^ORCID

Affiliation:

1. Dentofacial Deformities Research Center, Research Institute of Dental Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran.

2. Department of Oral and Maxillofacial Radiology, School of Dentistry, Shahid Beheshti University of Medical Sciences, Tehran, Iran.

3. Department of Trauma and Craniofacial Reconstruction, Queen Mary College, London, England.

4. Department of Operative Dentistry, University of Southern California, Los Angeles, CA, USA.

5. Dental School, Carol Davila University of Medical Sciences, Bucharest, Romania.

6. Discipline of Oral Surgery, Medicine and Diagnostics, School of Dentistry, Faculty of Medicine and Health, Westmead Centre for Oral Health, The University of Sydney, Sydney, Australia.

7. School of Dentistry, Islamic Azad University Tehran, Dental Branch, Tehran, Iran.

8. Department of Oral and Maxillofacial Radiology, Dental School, Islamic Azad University of Medical Sciences, Tehran, Iran.

Publisher

XMLink

Link

https://isdent.org/pdf/10.5624/isd.20240038

Reference21 articles.

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3. Convolutional neural networks: an overview and application in radiology