» Articles » PMID: 34233515

Current Applications and Development of Artificial Intelligence for Digital Dental Radiography

Overview
Date 2021 Jul 8
PMID 34233515
Citations 35
Authors
Affiliations
Soon will be listed here.
Abstract

In the last few years, artificial intelligence (AI) research has been rapidly developing and emerging in the field of dental and maxillofacial radiology. Dental radiography, which is commonly used in daily practices, provides an incredibly rich resource for AI development and attracted many researchers to develop its application for various purposes. This study reviewed the applicability of AI for dental radiography from the current studies. Online searches on PubMed and IEEE Xplore databases, up to December 2020, and subsequent manual searches were performed. Then, we categorized the application of AI according to similarity of the following purposes: diagnosis of dental caries, periapical pathologies, and periodontal bone loss; cyst and tumor classification; cephalometric analysis; screening of osteoporosis; tooth recognition and forensic odontology; dental implant system recognition; and image quality enhancement. Current development of AI methodology in each aforementioned application were subsequently discussed. Although most of the reviewed studies demonstrated a great potential of AI application for dental radiography, further development is still needed before implementation in clinical routine due to several challenges and limitations, such as lack of datasets size justification and unstandardized reporting format. Considering the current limitations and challenges, future AI research in dental radiography should follow standardized reporting formats in order to align the research designs and enhance the impact of AI development globally.

Citing Articles

Artificial intelligence-produced radiographic enhancements in dental clinical care: provider and patient perspectives.

Slashcheva L, Schroeder K, Heaton L, Cheung H, Prosa B, Ferrian N Front Oral Health. 2025; 6:1473877.

PMID: 40026368 PMC: 11868064. DOI: 10.3389/froh.2025.1473877.


Humanitarian forensic action in East Asia: where are we now? A concise review.

Zeng Z, Lv Y Forensic Sci Res. 2025; 10(1):owae014.

PMID: 40007635 PMC: 11850649. DOI: 10.1093/fsr/owae014.


Automated Age Estimation from OPG Images and Patient Records Using Deep Feature Extraction and Modified Genetic-Random Forest.

Ozlu Ucan G, Gwassi O, Apaydin B, Ucan B Diagnostics (Basel). 2025; 15(3).

PMID: 39941244 PMC: 11817095. DOI: 10.3390/diagnostics15030314.


Machine learning for automated identification of anatomical landmarks in ultrasound periodontal imaging.

Qi B, Sasi L, Khan S, Luo J, Chen C, Rahmani K Dentomaxillofac Radiol. 2025; 54(3):210-221.

PMID: 39775796 PMC: 11879227. DOI: 10.1093/dmfr/twaf001.


Artificial Intelligence-Assisted Segmentation of a Falx Cerebri Calcification on Cone-Beam Computed Tomography: A Case Report.

Issa J, Chidiac A, Mozdziak P, Kempisty B, Dorocka-Bobkowska B, Mehr K Medicina (Kaunas). 2025; 60(12.

PMID: 39768927 PMC: 11676691. DOI: 10.3390/medicina60122048.


References
1.
Vranckx M, Van Gerven A, Willems H, Vandemeulebroucke A, Ferreira Leite A, Politis C . Artificial Intelligence (AI)-Driven Molar Angulation Measurements to Predict Third Molar Eruption on Panoramic Radiographs. Int J Environ Res Public Health. 2020; 17(10). PMC: 7277237. DOI: 10.3390/ijerph17103716. View

2.
Nakamoto T, Taguchi A, Ohtsuka M, Suei Y, Fujita M, Tsuda M . A computer-aided diagnosis system to screen for osteoporosis using dental panoramic radiographs. Dentomaxillofac Radiol. 2008; 37(5):274-81. DOI: 10.1259/dmfr/68621207. View

3.
Akesson L, Hakansson J, Rohlin M . Comparison of panoramic and intraoral radiography and pocket probing for the measurement of the marginal bone level. J Clin Periodontol. 1992; 19(5):326-32. DOI: 10.1111/j.1600-051x.1992.tb00654.x. View

4.
Endres M, Hillen F, Salloumis M, Sedaghat A, Niehues S, Quatela O . Development of a Deep Learning Algorithm for Periapical Disease Detection in Dental Radiographs. Diagnostics (Basel). 2020; 10(6). PMC: 7344682. DOI: 10.3390/diagnostics10060430. View

5.
Lee J, Kim D, Jeong S, Choi S . Diagnosis and prediction of periodontally compromised teeth using a deep learning-based convolutional neural network algorithm. J Periodontal Implant Sci. 2018; 48(2):114-123. PMC: 5944222. DOI: 10.5051/jpis.2018.48.2.114. View