REVIEW ARTICLE
Radiographic assessment of caries severity, periodontal disease, and endodontic treatment – a narrative review of contemporary diagnostic indicators
1
Department of Conservative Dentistry and Endodontics, Pomeranian Medical University, Szczecin, Poland
2
Students’ Scientific Society of Endodontics at the Department of Conservative Dentistry and Endodontics, Pomeranian Medical University, Szczecin, Poland
Submission date: 2025-02-04
Final revision date: 2025-08-19
Acceptance date: 2025-08-21
Online publication date: 2026-07-07
Corresponding author
Katarzyna Lewusz-Butkiewicz
Department of Conservative Dentistry and Endodontics, Pomeranian Medical University in Szczecin, Powstańcow Wlkp. 72, 70-111 Szczecin, Poland
Department of Conservative Dentistry and Endodontics, Pomeranian Medical University in Szczecin, Powstańcow Wlkp. 72, 70-111 Szczecin, Poland
KEYWORDS
TOPICS
ABSTRACT
Radiology facilitates the imaging of two- and three-dimensional structures of hard tissues. Therefore, knowledge of radiological imaging techniques, their proper use, and knowledge of the latest advances have a significant impact on the efficiency of the dentist’s work. A comprehensive literature review was conducted based on available publications. Articles describing methods of radiological assessment of dental caries, periodontal disease, and endodontic treatment utilising using two-dimensional and three-dimensional imaging were included. The following diagnostic indices were introduced: ICCMS, PAI, CBCTPAI, and PESS. The potential of using artificial intelligence in radiological diagnostics were also presented. The necessity of two- and three-dimensional methodologies in contemporary dental diagnostics was also emphasised. Despite the enormous progress in spatial imaging, represented by CBCT, there are still some limitations in dental caries diagnostics. Furthermore, the integration of artificial intelligence in the domain of radiological diagnostics in dentistry is a promising field of science that requires further research.
REFERENCES (57)
1.
WHO. Global oral health status report: towards universal health coverage for oral health by 2030. WHO, Geneva 2022.
2.
Senel B, Kamburoglu K, Uçok O, Yüksel SP, Ozen T, Avsever H. Diagnostic accuracy of different imaging modalities in detection of proximal caries. Dentomaxillofac Radiol. 2010; 39(8): 501-511.
3.
Mouyen F, Benz C, Sonnabend E, Lodter JP. Presentation and physical evaluation of RadioVisioGraphy. Oral Surg Oral Med Oral Pathol. 1989; 68: 238-242.
4.
Shah N, Bansal N, Logani A. Recent advances in imaging technologies in dentistry. World J Radiol. 2014; 6(10): 794-807.
5.
Kidd EA, Pitts NB. A reappraisal of the value of the bitewing radiograph in the diagnosis of posterior approximal caries. Br Dent J. 1990; 169: 195-200.
6.
Setzer FC, Lee SM. Radiology in endodontics. Dent Clin North Am. 2021; 65(3): 475-486.
7.
Cotti E, Vargiu P, Dettori C, Mallarini G. Computerized tomography in the management and follow-up of extensive periapical lesion. Endod Dent Traumatol. 1999; 15(4): 186-189.
8.
Patel S, Dawood A, Whaites E, Pitt Ford T. New dimensions in endodontic imaging: part 1. Conventional and alternative radiographic systems. Int Endod J. 2009; 42(6): 447-462.
9.
Patel S, Brown J, Pimentel T, Kelly R, Abella F, Durack C. Cone beam computed tomography in endodontics: a review of the literature. Int Endod J. 2019; 52(9): 1138-1155.
10.
AAE and AAOMR Joint Position Statement: Use of Cone Beam Computed Tomography in Endodontics – 2015/2016 Update. J Endod. 2015; 41(9): 1393-1396.
11.
Queiroz PM, Santaella GM, Groppo FC, Freitas DQ. Metal artifact production and reduction in CBCT with different numbers of basis images. Imaging Sci Dent. 2018; 48(1): 41-44.
12.
Bader JD, Shugars DA, Bonito AJ. Systematic reviews of selected dental caries diagnostic and management methods. J Dent Educ. 2001; 65(10): 960-968.
13.
Fasihinia H, Khalesi M, Gholami L. Dental caries diagnostic methods. Avicenna J Dent Res. 2010; 1(2): 1-2.
14.
Saffarpour A, Tonkaboni A, Pareshkooh MK, Nazari SOA, Manafi S, Kharazifard MJ. Comparison of diagnostic effects of infrared imaging, visual inspection, and bitewing radiography in primary occlusal caries of permanent teeth. Dent Res J (Isfahan). 2023; 20: 68.
15.
Kamburoglu K, Kolsuz E, Murat S, Yüksel S, Ozen T. Proximal caries detection accuracy using intraoral bitewing radiography, extraoral bitewing radiography and panoramic radiography. Dentomaxillofac Radiol. 2012; 41: 450-459.
16.
Abu El-Ela WH, Farid MM, Mostafa MS. Intraoral versus extraoral bitewing radiography in detection of enamel proximal caries: an ex vivo study. Dentomaxillofac Radiol. 2016; 45(4): 20150326.
17.
Biggerstaff RH, Phillips JR. A quantitative comparison of paralleling long-cone and bisection-of-angle periapical radiography. Oral Surg Oral Med Oral Pathol. 1976; 62(5): 673-677.
18.
Estrela C, Bueno MR, Azevedo BC, Azevedo JR, Pécora JD. A new periapical index based on cone beam computed tomography. J Endod. 2008; 34(11): 1325-1331.
19.
Aminoshariae A, Kulild JC, Syed A. Cone-beam computed tomography compared with intraoral radiographic lesions in endodontic outcome studies: a systematic review. J Endod. 2018; 44(11): 1626-1631.
20.
Sharan A, Madjar D. Correlation between maxillary sinus floor topography and related root position of posterior teeth using panoramic and cross-sectional computed tomography imaging. Oral Surg Oral Med Oral Pathol Oral Radiol Endod. 2006; 102(3): 375-381.
21.
Shahbazian M, Vandewoude C, Wyatt J, Jacobs R. Comparative assessment of panoramic radiography and CBCT imaging for radiodiagnostics in the posterior maxilla. Clin Oral Investig. 2014; 18(1): 293-300.
22.
Patel S, Durack C, Abella F, Shemesh H, Roig M, Lambrechts P, Lemberg K. Cone beam computed tomography in endodontics – a review. Int Endod J. 2015; 48(1): 3-15.
23.
Suomalainen A, Pakbaznejad EE, Robinson S. Dentomaxillofacial imaging with panoramic views and cone beam CT. Insights Imaging. 2015; 6(1): 1-16.
24.
Lindfors N, Ekestubbe A, Frisk F, Lund H. Is cone-beam computed tomography (CBCT) an alternative to plain radiography in assessments of dental disease? Clin Oral Investig. 2024; 28(2): 127.
25.
Mosavat F, Ahmadi E, Amirfarhangi S, Rafeie N. Evaluation of diagnostic accuracy of CBCT and intraoral radiography for proximal caries detection in the presence of different dental restoration materials. BMC Oral Health. 2023; 23: 419.
26.
Abu El-Ela WH, Farid MM, Abou El-Fotouh M. The impact of different dental restorations on detection of proximal caries by cone beam computed tomography. Clin Oral Investig. 2022; 26(3): 2413-2420.
27.
Patel S, Durack C, Abella F, Roig M, Shemesh H, Lambrechts P, Lemberg K. European Society of Endodontology position statement: the use of CBCT in endodontics. Int Endod J. 2014; 47(5): 502-504.
28.
Agustsdottir H, Gudmundsdottir H, Eggertsson H, et al. Caries prevalence of permanent teeth: a national survey of children in Iceland using ICDAS. Community Dent Oral Epidemiol. 2010; 38(4): 299-309.
29.
Ekstrand KR, Luna LE, Promisiero L, Cortes A, Cuevas S, Reyes JF, Torres CE, Martignon S. The reliability and accuracy of two methods for proximal caries detection and depth on directly visible proximal surfaces: an in vitro study. Caries Res. 2011; 45(2): 93-99.
30.
Young DA, Nový BB, Zeller GG, Hale R, Hart TC, Truelove EL; American Dental Association Council on Scientific Affairs. The American Dental Association Caries Classification System for clinical practice. J Am Dent Assoc. 2015; 146(2): 79-86.
31.
Azarpazhooh A, Sgro A, Cardoso E, Elbarbary M, Laghapour Lighvan N, Badewy R, Malkhassian G, Jafarzadeh H, Bakhtiar H, Khazaei S, Oren A, Gerbig M, He H, Kishen A, Shah PS. A scoping review of 4 decades of outcomes in nonsurgical root canal treatment, nonsurgical retreatment, and apexification studies – Part 2: outcome measures. J Endod. 2022; 48(1): 29-39.
32.
Archana D, Gopikrishna V, Gutmann JL, Savadamoorthi KS, Kumar AR, Narayanan LL. Prevalence of periradicular radiolucencies and its association with the quality of root canal procedures and coronal restorations in an adult urban Indian population. J Conserv Dent. 2015; 18(1): 34-38.
33.
Kielbassa AM, Frank W, Madaus T. Radiologic assessment of quality of root canal fillings and periapical status in an Austrian subpopulation – an observational study. PLoS One. 2017; 12(12): e0176724.
34.
Paes da Silva Ramos Fernandes LM, Ordinola-Zapata R, Húngaro Duarte MA, Alvares Capelozza AL. Prevalence of apical periodontitis detected in cone beam CT images of a Brazilian subpopulation. Dentomaxillofac Radiol. 2013; 42(1): 80179163.
35.
Huumonen S, Suominen AL, Vehkalahti MM. Prevalence of apical periodontitis in root-filled teeth: findings from a nationwide survey in Finland. Int Endod J. 2017; 50(3): 229-236.
36.
Kamberi B, Hoxha V, Stavileci M, Dragusha E, Kuçi A, Kqiku L. Prevalence of apical periodontitis and endodontic treatment in a Kosovar adult population. BMC Oral Health. 2011; 11: 32.
37.
Van der Veken D, Curvers F, Fieuws S, Lambrechts P. Prevalence of apical periodontitis and root-filled teeth in a Belgian subpopulation found on CBCT images. Int Endod J. 2017; 50(4): 317-329.
38.
Ørstavik D, Kerekes K, Eriksen HM. The periapical index: a scoring system for radiographic assessment of apical periodontitis. Endod Dent Traumatol. 1986; 2(1): 20-34.
39.
Ng YL, Mann V, Gulabivala K. Outcome of secondary root canal treatment: a systematic review of the literature. Int Endod J. 2008; 41(12): 1026-1046.
40.
Ng YL, Mann V, Rahbaran S, Lewsey J, Gulabivala K. Outcome of primary root canal treatment: systematic review of the literature – part 2. Influence of clinical factors. Int Endod J. 2008; 41(1): 6-31.
41.
Venskutonis T, Plotino G, Tocci L, Gambarini G, Maminskas J, Juodzbalys G. Periapical and endodontic status scale based on periapical bone lesions and endodontic treatment quality evaluation using CBCT. J Endod. 2015; 41(2): 190-196.
42.
Thrall JH, Li X, Li Q, Cruz C, Do S, Dreyer K, Brink J. Artificial intelligence and machine learning in radiology: opportunities, challenges, pitfalls, and criteria for success. J Am Coll Radiol. 2018; 15(3 Pt B): 504-508.
43.
Putra RH, Doi C, Yoda N, Astuti ER, Sasaki K. Current applications and development of artificial intelligence for digital dental radiography. Dentomaxillofac Radiol. 2022; 51(1): 20210197.
45.
Li Z, Liu F, Yang W, Peng S, Zhou J. A survey of convolutional neural networks: analysis, applications, and prospects. IEEE Trans Neural Netw Learn Syst. 2022; 33(12): 6999-7019.
46.
Yamashita R, Nishio M, Do RKG, Togashi K. Convolutional neural networks: an overview and application in radiology. Insights Imaging. 2018; 9(4): 611-629.
47.
Lee S, Oh SI, Jo J, Kang S, Shin Y, Park JW. Deep learning for early dental caries detection in bitewing radiographs. Sci Rep. 2021; 11: 16807.
48.
Moran M, Faria M, Giraldi G, Bastos L, Oliveira L, Conci A. Classification of approximal caries in bitewing radiographs using convolutional neural networks. Sensors (Basel). 2021; 21(15): 5192.
49.
Alsolamy M, Nadeem F, Azhari AA, Ahmed WM. Automated detection, localization, and severity assessment of proximal dental caries from bitewing radiographs using deep learning. Diagnostics (Basel). 2025; 15(7): 899.
50.
Zhu H, Cao Z, Lian L, Ye G, Gao H, Wu J. CariesNet: a deep learning approach for segmentation of multi-stage caries lesion from oral panoramic X-ray image. Neural Comput Appl. 2022; 34: 13425-13438.
51.
Ekert T, Krois J, Meinhold L, Elhennawy K, Emara R, Gol- la T, Schwendicke F. Deep learning for the radiographic detection of apical lesions. J Endod. 2019; 45(7): 917-922.e5.
52.
Aminoshariae A, Kulild J, Nagendrababu V. Artificial intelligence in endodontics: current applications and future directions. J Endod. 2021; 47(9): 1352-1357.
53.
Brady AP, Neri E. Artificial intelligence in radiology – ethical considerations. Diagnostics (Basel). 2020; 10(4): 231.
54.
Bonny T, Al Nassan W, Obaideen K, Al Mallahi MN, Mohammad Y, El-Damanhoury HM. Contemporary role and applications of artificial intelligence in dentistry. F1000Research. 2023; 12: 1179.
55.
ADA Council on Scientific Affairs. The use of dental radiographs: update and recommendations. J Am Dent Assoc. 2012; 143(5): 573-575.
56.
Boztuna B, Yilmaz E, Kaya M, Demircioglu Y, Ozkan S, Aydin H. Segmentation of periapical lesions with automatic deep learning on panoramic radiographs. BMC Oral Health. 2024; 24(1): 5126.
57.
Azad R, Aghdam EK, Rauland A, Jia Y, Avval AH, Bozorgpour A, et al. Medical image segmentation review: the success of U-Net. IEEE Trans Pattern Anal Mach Intell. 2024; 46(5): 1234-1256.
Share
RELATED ARTICLE
| eISSN: | 2300-6722 |
| ISSN: | 1899-1874 |
We process personal data collected when visiting the website. The function of obtaining information about users and their behavior is carried out by voluntarily entered information in forms and saving cookies in end devices. Data, including cookies, are used to provide services, improve the user experience and to analyze the traffic in accordance with the Privacy policy. Data are also collected and processed by Google Analytics tool (more).
You can change cookies settings in your browser. Restricted use of cookies in the browser configuration may affect some functionalities of the website.
You can change cookies settings in your browser. Restricted use of cookies in the browser configuration may affect some functionalities of the website.
