Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2024 Feb 24;24(1):274.
doi: 10.1186/s12903-024-04046-7.

Artificial intelligence for radiographic imaging detection of caries lesions: a systematic review

Domenico Albano  1   2 , Vanessa Galiano  3 , Mariachiara Basile  4 , Filippo Di Luca  4 , Salvatore Gitto  5   6 , Carmelo Messina  5   6 , Maria Grazia Cagetti  7 , Massimo Del Fabbro  7   8 , Gianluca Martino Tartaglia  7   8 , Luca Maria Sconfienza  5   6
Affiliations

Artificial intelligence for radiographic imaging detection of caries lesions: a systematic review

Domenico Albano et al. BMC Oral Health. .

Abstract

Background: The aim of this systematic review is to evaluate the diagnostic performance of Artificial Intelligence (AI) models designed for the detection of caries lesion (CL).

Materials and methods: An electronic literature search was conducted on PubMed, Web of Science, SCOPUS, LILACS and Embase databases for retrospective, prospective and cross-sectional studies published until January 2023, using the following keywords: artificial intelligence (AI), machine learning (ML), deep learning (DL), artificial neural networks (ANN), convolutional neural networks (CNN), deep convolutional neural networks (DCNN), radiology, detection, diagnosis and dental caries (DC). The quality assessment was performed using the guidelines of QUADAS-2.

Results: Twenty articles that met the selection criteria were evaluated. Five studies were performed on periapical radiographs, nine on bitewings, and six on orthopantomography. The number of imaging examinations included ranged from 15 to 2900. Four studies investigated ANN models, fifteen CNN models, and two DCNN models. Twelve were retrospective studies, six cross-sectional and two prospective. The following diagnostic performance was achieved in detecting CL: sensitivity from 0.44 to 0.86, specificity from 0.85 to 0.98, precision from 0.50 to 0.94, PPV (Positive Predictive Value) 0.86, NPV (Negative Predictive Value) 0.95, accuracy from 0.73 to 0.98, area under the curve (AUC) from 0.84 to 0.98, intersection over union of 0.3-0.4 and 0.78, Dice coefficient 0.66 and 0.88, F1-score from 0.64 to 0.92. According to the QUADAS-2 evaluation, most studies exhibited a low risk of bias.

Conclusion: AI-based models have demonstrated good diagnostic performance, potentially being an important aid in CL detection. Some limitations of these studies are related to the size and heterogeneity of the datasets. Future studies need to rely on comparable, large, and clinically meaningful datasets.

Protocol: PROSPERO identifier: CRD42023470708.

Keywords: Artificial intelligence; Caries lesion; Detection; Diagnosis; Radiographic imaging.

PubMed Disclaimer

Conflict of interest statement

The authors declare no competing interests.

Figures

Fig. 1
Fig. 1
Flow chart of articles’ screening and selection
Fig. 2
Fig. 2
QUADAS-2 assessment of the individual risk of bias domains and applicability concerns
See this image and copyright information in PMC

References

    1. Norman Tinanoff. 12 - Dental Caries, Editor(s): Arthur J. Nowak, John R. Christensen, Tad R. Mabry, Janice A. Townsend, Martha H. Wells, Pediatric Dentistry (Sixth Edition), Elsevier, 2019, Pages 169–179, ISBN 9780323608268, 10.1016/B978-0-323-60826-8.00012-2.
    1. Dayo AF, Wolff MS, Syed AZ, Mupparapu M. Radiology of Dental Caries. Dent Clin North Am. 2021;65(3):427–45. doi: 10.1016/j.cden.2021.02.002. - DOI - PubMed
    1. Khanagar SB, Alfouzan K, Awawdeh M, Alkadi L, Albalawi F, Alfadley A. Application and performance of Artificial Intelligence Technology in Detection, diagnosis and prediction of Dental Caries (DC)-A systematic review. Diagnostics (Basel) 2022;12(5):1083. doi: 10.3390/diagnostics12051083. - DOI - PMC - PubMed
    1. Albano D, Gitto S, Messina C, Serpi F, Salvatore C, Castiglioni I, Zagra L, De Vecchi E, Sconfienza LM. MRI-based artificial intelligence to predict infection following total hip arthroplasty failure. Radiol Med. 2023;128(3):340–6. doi: 10.1007/s11547-023-01608-7. - DOI - PMC - PubMed
    1. Chianca V, Cuocolo R, Gitto S, Albano D, Merli I, Badalyan J, Cortese MC, Messina C, Luzzati A, Parafioriti A, Galbusera F, Brunetti A, Sconfienza LM. Radiomic Machine Learning Classifiers in Spine Bone tumors: a Multi-Software, Multi-scanner Study. Eur J Radiol. 2021;137:109586. doi: 10.1016/j.ejrad.2021.109586. - DOI - PubMed

Publication types