Imaging Sci Dent.
2024 Sep;54(3):232-239. 10.5624/isd.20240038.
Evaluation of deep learning and convolutional neural network algorithms for mandibular fracture detection using radiographic images: A systematic review and meta-analysis
- Affiliations
-
- 1Dentofacial Deformities Research Center, Research Institute of Dental Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran
- 2Department of Oral and Maxillofacial Radiology, School of Dentistry, Shahid Beheshti University of Medical Sciences, Tehran, Iran
- 3Department of Trauma and Craniofacial Reconstruction, Queen Mary College, London, England
- 4Department of Operative Dentistry, University of Southern California, Los Angeles, CA, USA
- 5Dental School, Carol Davila University of Medical Sciences, Bucharest, Romania
- 6Discipline 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
- 7School of Dentistry, Islamic Azad University Tehran, Dental Branch, Tehran, Iran
- 8Department of Oral and Maxillofacial Radiology, Dental School, Islamic Azad University of Medical Sciences, Tehran, Iran
- KMID: 2559832
- DOI: http://doi.org/10.5624/isd.20240038
Abstract
- Purpose
The use of artificial intelligence (AI) and deep learning algorithms in dentistry, especially for processing radiographic images, has markedly increased. However, detailed information remains limited regarding the accuracy of these algorithms in detecting mandibular fractures.
Materials and Methods
This meta-analysis was conducted in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Specific keywords were generated regarding the accuracy of AI algorithms in detecting mandibular fractures on radiographic images. Then, the PubMed/Medline, Scopus, Embase, and Web of Science databases were searched. The Quality Assessment of Diagnostic Accuracy Studies 2 (QUADAS-2) tool was employed to evaluate potential bias in the selected studies. A pooled analysis of the relevant parameters was conducted using STATA version 17 (StataCorp, College Station, TX, USA), utilizing the metandi command.
Results
Of the 49 studies reviewed, 5 met the inclusion criteria. All of the selected studies utilized convolutional neural network algorithms, albeit with varying backbone structures, and all evaluated panoramic radiography images. The pooled analysis yielded a sensitivity of 0.971 (95% confidence interval [CI]: 0.881-0.949), a specificity of 0.813 (95% CI: 0.797-0.824), and a diagnostic odds ratio of 7.109 (95% CI: 5.27-8.913).
Conclusion
This review suggests that deep learning algorithms show potential for detecting mandibular fractures on panoramic radiography images. However, their effectiveness is currently limited by the small size and narrow scope of available datasets. Further research with larger and more diverse datasets is crucial to verify the accuracy of these tools in in practical dental settings.
- Full Text Links
-
- Actions
-
Cited
- CITED
-
- Close
- Share
-
- Similar articles
-
- Deep learning convolutional neural network algorithms for the early detection and diagnosis of dental caries on periapical radiographs: A systematic review
- Detection and Weak Segmentation of Masses in Gray-Scale Breast Mammogram Images Using Deep Learning
- Obstructive sleep apnoea detection using convolutional neural network based deep learning framework
- Artificial Intelligence Detection of Cervical Spine Fractures Using Convolutional Neural Network Models
- Deep learning algorithms for identifying 79 dental implant types
