Radiographic Evaluation Of Periapical Lesions: From Conventional Imaging To Artificial Intelligence
Özet
Periapical lesions remain one of the radiographic findings most frequently encountered in endodontic care and continue to influence diagnosis, treatment planning, and follow-up decisions. Their assessment has progressed from conventional two-dimensional projections to three-dimensional imaging and, more recently, to AI-assisted interpretation workflows. This chapter outlines the imaging appearance of periapical lesions, compares the advantages and drawbacks of conventional and advanced modalities, and summarizes how machine learning and deep learning are being applied to lesion detection, segmentation, classification, and prognostic assessment. The chapter particularly emphasizes practical interpretation, common diagnostic challenges, and the appropriate integration of AI within clinician-directed imaging pathways.
Referanslar
Orhan K, Bayrakdar I, Ezhov M, et al. Evaluation of artificial intelligence for detecting periapical pathosis on cone‐beam computed tomography scans. International Endodontic Journal. 2020;53(5):680-689. https://doi.org/10.1111/iej.13265
Ketenci Çay F, Yeşil Ç, Çay O, et al. DeepLabv3+ method for detecting and segmenting apical lesions on panoramic radiography. Clinical Oral Investigations. 2025;29(2):101. https://doi.org/10.1007/s00784-025-06156-0
Tsai P, Torabinejad M, Rice D, et al. Accuracy of cone-beam computed tomography and periapical radiography in detecting small periapical lesions. Journal of Endodontics. 2012;38(7):965-970. https://doi.org/10.1016/j.joen.2012.03.001.
Davies A, Mannocci F, Mitchell P, et al. The detection of periapical pathoses in root filled teeth using single and parallax periapical radiographs versus cone beam computed tomography–a clinical study. International Endodontic Journal. 2015;48(6):582-592. https://doi.org/10.1111/iej.12352
Pope O, Sathorn C, Parashos P. A comparative investigation of cone-beam computed tomography and periapical radiography in the diagnosis of a healthy periapex. Journal of Endodontics. 2014;40(3):360-365. https://doi.org/10.1016/j.joen.2013.10.003
Patel S, Wilson R, Dawood A, et al. The detection of periapical pathosis using digital periapical radiography and cone beam computed tomography–part 2: a 1‐year post‐treatment follow‐up. International Endodontic Journal. 2012;45(8):711-723. https://doi.org/10.1111/j.1365-2591.2012.02076.x
Estrela C, Bueno MR, Leles CR, et al. Accuracy of cone beam computed tomography and panoramic and periapical radiography for detection of apical periodontitis. Journal of Endodontics. 2008;34(3):273-279. https://doi.org/10.1016/j.joen.2007.11.023
Siqueira Jr JF, Silva WO, Romeiro K, et al. Apical root canal microbiome associated with primary and posttreatment apical periodontitis: A systematic review. International Endodontic Journal. 2024;57(8):1043-1058. https://doi.org/10.1111/iej.14071
Kakehashi S, Stanley H, Fitzgerald R. The effects of surgical exposures of dental pulps in germ-free and conventional laboratory rats. Oral Surgery, Oral Medicine, Oral Pathology. 1965;20(3):340-349. https://doi.org/10.1016/0030-4220(65)90166-0
Stashenko P, Yu SM, Wang C-Y. Kinetics of immune cell and bone resorptive responses to endodontic infections. Journal of Endodontics. 1992;18(9):422-426. https://doi.org/10.1016/S0099-2399(06)80841-1
Bender I, Seltzer S. Roentgenographic and direct observation of experimental lesions in bone: I. The Journal of the American Dental Association. 1961;62(2):152-160. https://doi.org/10.14219/jada.archive.1961.0030
Bender I. Factors influencing the radiographic appearance of bony lesions. Journal of Endodontics. 1982;8(4):161-170. https://doi.org/10.1016/S0099-2399(82)80212-4
Ekert T, Krois J, Meinhold L, et al. Deep learning for the radiographic detection of apical lesions. Journal of Endodontics. 2019;45(7):917-922. e5. https://doi.org/10.1016/j.joen.2019.03.016.
Ezhov M, Gusarev M, Golitsyna M, et al. Clinically applicable artificial intelligence system for dental diagnosis with CBCT. Scientific Reports. 2021;11(1):15006. https://doi.org/10.1038/s41598-021-94093-9
Banomyong D, Arayasantiparb R, Sirakulwat K, et al. Association between clinical/radiographic characteristics and histopathological diagnoses of periapical granuloma and cyst. European Journal of Dentistry. 2023;17(04):1241-1247. https://doi.org/10.1055/s-0042-1759489
Rosenberg PA, Frisbie J, Lee J, et al. Evaluation of pathologists (histopathology) and radiologists (cone beam computed tomography) differentiating radicular cysts from granulomas. Journal of Endodontics. 2010;36(3):423-428. https://doi.org/10.1016/j.joen.2009.11.005
Modi K, Padmapriya R, Elango S, et al. Nonmalignant nonendodontic lesions mimicking periapical lesions of endodontic origin: A systematic review. Journal of Conservative Dentistry and Endodontics. 2022;25(3):214-225. https://doi.org/10.4103/jcd.jcd_13_22
Patel S, Dawood A, Mannocci F, et al. Detection of periapical bone defects in human jaws using cone beam computed tomography and intraoral radiography. International Endodontic Journal. 2009;42(6):507-515. https://doi.org/10.1111/j.1365-2591.2008.01538.x
Forsberg J, Halse A. Radiographic simulation of a periapieal lesion comparing the paralleling and the bisecting‐angle techniques. International Endodontic Journal. 1994;27(3):133-138.
https://doi.org/10.1111/j.1365-2591.1994.tb00242.x
Paurazas SB, Geist JR, Pink FE, et al. Comparison of diagnostic accuracy of digital imaging by using CCD and CMOS-APS sensors with E-speed film in the detection of periapical bony lesions. Oral Surgery, Oral Medicine, Oral Pathology, Oral Radiology, and Endodontology. 2000;89(3):356-362. https://doi.org/10.1016/S1079-2104(00)70102-8
Goldman M, Pearson AH, Darzenta N. Endodontic success—who's reading the radiograph? Oral Surgery, Oral Medicine, Oral Pathology. 1972;33(3):432-437. https://doi.org/10.1016/0030-4220(72)90473-2
Patel S, Brown J, Semper M, et al. European Society of Endodontology position statement: Use of cone beam computed tomography in Endodontics: European Society of Endodontology (ESE) developed by. International Endodontic Journal. 2019;52(12):1675-1678.
https://doi.org/10.1111/iej.13187
European Society of Endodontology. Quality guidelines for endodontic treatment: consensus report of the European Society of Endodontology. International Endodontic Journal. 2006;39(12):921-930. https://doi.org/10.1111/j.1365-2591.2006.01180.x
Lo Giudice R, Nicita F, Puleio F, et al. Accuracy of periapical radiography and CBCT in endodontic evaluation. International Journal of Dentistry. 2018;2018(1):2514243.
https://doi.org/10.1155/2018/2514243
Farman AG, Farman TT. A comparison of 18 different x-ray detectors currently used in dentistry. Oral Surgery, Oral Medicine, Oral Pathology, Oral Radiology, and Endodontology. 2005;99(4):485-489. https://doi.org/10.1016/j.tripleo.2004.04.002
Strindberg LZ. The dependence of the results of pulp therapy on certain factors: an analytic study based on radiographic and clinical follow-up examination. Acta Odontologica Scandinavica. 1956.
Dutra KL, Haas L, Porporatti AL, et al. Diagnostic accuracy of cone-beam computed tomography and conventional radiography on apical periodontitis: a systematic review and meta-analysis. Journal of Endodontics. 2016;42(3):356-64. https://doi.org/10.1016/j.joen.2015.12.015
White SC, Pharoah MJ. Oral radiology Principles and interpretation. 7th ed. Elsevier Health Sciences; 2014.
Kanagasingam S, Hussaini H, Soo I, et al. Accuracy of single and parallax film and digital periapical radiographs in diagnosing apical periodontitis–a cadaver study. International Endodontic Journal. 2017;50(5):427-436. https://doi.org/10.1111/iej.12651
Nardi C, Calistri L, Pradella S, et al. Accuracy of orthopantomography for apical periodontitis without endodontic treatment. Journal of Endodontics. 2017;43(10):1640-1646. https://doi.org/10.1016/j.joen.2017.06.020
Nardi C, Calistri L, Grazzini G, et al. Is panoramic radiography an accurate imaging technique for the detection of endodontically treated asymptomatic apical periodontitis? Journal of Endodontics. 2018;44(10):1500-1508. https://doi.org/10.1016/j.joen.2018.07.003
Huumonen S, Ørstavik D. Radiological aspects of apical periodontitis. Endodontic Topics. 2002;1(1):3-25. https://doi.org/10.1034/j.1601-1546.2002.10102.x
Patel S. New dimensions in endodontic imaging: Part 2. Cone beam computed tomography. International Endodontic Journal. 2009;42(6):463-475. https://doi.org/10.1111/j.1365-2591.2008.01531.x
Ørstavik D, Kerekes K, Eriksen HM. The periapical index: a scoring system for radiographic assessment of apical periodontitis. Dental Traumatology. 1986;2(1):20-
https://doi.org/10.1111/j.1600-9657.1986.tb00119.x
Lee SJ, Messer H. Radiographic appearance of artificially prepared periapical lesions confined to cancellous bone. International Endodontic Journal. 1986;19(2):64-72. https://doi.org/10.1111/j.1365-2591.1986.tb00894.x
Parker JM, Mol A, Rivera EM, et al. Cone-beam computed tomography uses in clinical endodontics: observer variability in detecting periapical lesions. Journal of Endodontics. 2017;43(2):184-7. https://doi.org/10.1016/j.joen.2016.10.007
Lofthag-Hansen S, Huumonen S, Gröndahl K, et al. Limited cone-beam CT and intraoral radiography for the diagnosis of periapical pathology. Oral Surgery, Oral Medicine, Oral Pathology, Oral Radiology, and Endodontology. 2007;103(1):114-119. https://doi.org/10.1016/j.tripleo.2006.01.001
Abella F, Patel S, Duran-Sindreu F, et al. Evaluating the periapical status of teeth with irreversible pulpitis by using cone-beam computed tomography scanning and periapical radiographs. Journal of Endodontics. 2012;38(12):1588-1591. https://doi.org/10.1016/j.joen.2012.09.003
Scarfe WC, Farman AG. What is cone-beam CT and how does it work? Dental Clinics of North America. 2008;52:707-730. https://doi.org/10.1016/j.cden.2008.05.005
Velvart P, Hecker H, Tillinger G. Detection of the apical lesion and the mandibular canal in conventional radiography and computed tomography. Oral Surgery, Oral Medicine, Oral Pathology, Oral Radiology, and Endodontology. 2001;92(6):682-688. https://doi.org/10.1067/moe.2001.118904
Durack C, Patel S, Davies J, et al. Diagnostic accuracy of small volume cone beam computed tomography and intraoral periapical radiography for the detection of simulated external inflammatory root resorption. International Endodontic Journal. 2011;44(2):136-147. https://doi.org/10.1111/j.1365-2591.2010.01819.x
Blattner TC, George N, Lee CC, et al. Efficacy of cone-beam computed tomography as a modality to accurately identify the presence of second mesiobuccal canals in maxillary first and second molars: a pilot study. Journal of Endodontics. 2010;36(5):867-870. https://doi.org/10.1016/j.joen.2009.12.023
Bornstein MM, Lauber R, Sendi P, et al. Comparison of periapical radiography and limited cone-beam computed tomography in mandibular molars for analysis of anatomical landmarks before apical surgery. Journal of Endodontics. 2011;37(2):151-157. https://doi.org/10.1016/j.joen.2010.11.014
Kruse C, Spin‐Neto R, Evar Kraft D, et al. Diagnostic accuracy of cone beam computed tomography used for assessment of apical periodontitis: an ex vivo histopathological study on human cadavers. International Endodontic Journal. 2019;52(4):439-450. https://doi.org/10.1111/iej.13020
Estrela C, Bueno MR, Azevedo BC, et al. A new periapical index based on cone beam computed tomography. Journal of Endodontics. 2008;34(11):1325-1331. https://doi.org/10.1016/j.joen.2008.08.013
Aggarwal V, Singla M. Use of computed tomography scans and ultrasound in differential diagnosis and evaluation of nonsurgical management of periapical lesions. Oral Surgery, Oral Medicine, Oral Pathology, Oral Radiology, and Endodontology. 2010;109(6):917-923. https://doi.org/10.1016/j.tripleo.2009.12.055
Juerchott A, Pfefferle T, Flechtenmacher C, et al. Differentiation of periapical granulomas and cysts by using dental MRI: a pilot study. International Journal of Oral Science. 2018;10(2):17. https://doi.org/10.1038/s41368-018-0017-y
Cotti E, Campisi G, Garau V, et al. A new technique for the study of periapical bone lesions: ultrasound real time imaging. International Endodontic Journal. 2002;35(2):148-152. https://doi.org/10.1046/j.1365-2591.2002.00458.x
Hilmi A, Patel S, Mirza K, et al. Efficacy of imaging techniques for the diagnosis of apical periodontitis: A systematic review. International Endodontic Journal. 2023;56:326-339. https://doi.org/10.1111/iej.13921
Rechenberg D, Munir A, Zehnder M. Correlation between the clinically diagnosed inflammatory process and periapical index scores in severely painful endodontically involved teeth. International Endodontic Journal. 2021;54(2):172-180. https://doi.org/10.1111/iej.13407
Chau K-K, Zhu M, AlHadidi A, et al. A novel AI model for detecting periapical lesion on CBCT: CBCT-SAM. Journal of Dentistry. 2025;153:105526. https://doi.org/10.1016/j.jdent.2024.105526
Levy DH, Dinur N, Becker T, et al. Use of cone-beam computed tomography as a critical component in the diagnosis of an infected nasopalatine duct cyst mimicking chronic apical abscess: a case report. Journal of Endodontics. 2021;47(7):1177-1181. https://doi.org/10.1016/j.joen.2021.04.018
Brody A, Zalatnai A, Csomo K, et al. Difficulties in the diagnosis of periapical translucencies and in the classification of cemento-osseous dysplasia. BMC Oral Health. 2019;19(1):139. https://doi.org/10.1186/s12903-019-0843-0
Liao W-C, Chen C-H, Pan Y-H, et al. Vertical root fracture in non-endodontically and endodontically treated teeth: current understanding and future challenge. Journal of Personalized Medicine. 2021;11(12):1375. https://doi.org/10.3390/jpm11121375
Natanasabapathy V, Arul B, Mishra A, et al. Ultrasound imaging for the differential diagnosis of periapical lesions of endodontic origin in comparison with histopathology–a systematic review and meta‐analysis. International Endodontic Journal. 2021;54(5):693-711. https://doi.org/10.1111/iej.13465
Pontes FSC, Fonseca FP, de Jesus AS, et al. Nonendodontic lesions misdiagnosed as apical periodontitis lesions: series of case reports and review of literature. Journal of Endodontics. 2014;40(1):16-27. https://doi.org/10.1016/j.joen.2013.08.021
Patel S, Durack C, Abella F, et al. Cone beam computed tomography in e ndodontics–a review. International Endodontic Journal. 2015;48(1):3-15. https://doi.org/10.1111/iej.12270
Fu W, Zhu Q, Li N, et al. Clinically oriented CBCT periapical lesion evaluation via 3D CNN algorithm. Journal of Dental Research. 2024;103(1):5-12. https://doi.org/10.1177/00220345231201793
Fernández R, Cadavid D, Zapata SM, et al. Impact of three radiographic methods in the outcome of nonsurgical endodontic treatment: a five-year follow-up. Journal of Endodontics. 2013;39(9):1097-1103. https://doi.org/10.1016/j.joen.2013.04.002
Schwendicke Fa, Samek W, Krois J. Artificial intelligence in dentistry: chances and challenges. Journal of Dental Research. 2020;99(7):769-774. https://doi.org/10.1177/0022034520915714
Hung K, Yeung AWK, Tanaka R, et al. Current applications, opportunities, and limitations of AI for 3D imaging in dental research and practice. International Journal of Environmental Research and Public Health. 2020;17(12):4424. https://doi.org/10.3390/ijerph17124424
Krizhevsky A, Sutskever I, Hinton GE. Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems. 2012;25. https://doi.org/10.1145/3065386
Aloufi AS. Detection of Periapical Lesions Using Artificial Intelligence: A Narrative Review. Diagnostics. 2026;16(2):301. https://doi.org/10.3390/diagnostics16020301
Khanagar SB, Alfadley A, Alfouzan K, et al. Developments and performance of artificial intelligence models designed for application in endodontics: a systematic review. Diagnostics. 2023;13(3):414. https://doi.org/10.3390/diagnostics13030414
Setzer F, Li J, Khan A. The use of artificial intelligence in endodontics. Journal of Dental Research. 2024;103(9):853-62. https://doi.org/10.1177/00220345241255593
Boubaris M, Cameron A, Manakil J, et al. Artificial intelligence vs. semi-automated segmentation for assessment of dental periapical lesion volume index score: a cone-beam CT study. Computers in Biology and Medicine. 2024;175:108527. https://doi.org/10.1016/j.compbiomed.2024.108527
Klein V, Büttner M, Göstemeyer G, et al. From inconsistent annotations to ground truth: Aggregation strategies for annotations of proximal carious lesions in dental imagery. Journal of Dentistry. 2025;157:105728. https://doi.org/10.1016/j.jdent.2025.105728
Allihaibi M, Koller G, Mannocci F. Diagnostic accuracy of a commercial AI-based platform in evaluating endodontic treatment outcomes on periapical radiographs using CBCT as the reference standard. Journal of Endodontics. 2025;51(7):898-908. e8. https://doi.org/10.1016/j.joen.2025.03.007
Sadr S, Mohammad-Rahimi H, Motamedian SR, et al. Deep learning for detection of periapical radiolucent lesions: a systematic review and meta-analysis of diagnostic test accuracy. Journal of Endodontics. 2023;49(3):248-261. e3. https://doi.org/10.1016/j.joen.2022.12.007
Campo L, Aliaga IJ, De Paz JF, et al. Retreatment predictions in odontology by means of CBR systems. Computational intelligence and neuroscience. 2016;2016(1):7485250. https://doi.org/10.1155/2016/7485250
Liu J, Jin C, Wang X, et al. A comparative analysis of deep learning models for assisting in the diagnosis of periapical lesions in periapical radiographs. BMC Oral Health. 2025;25(1):801. https://doi.org/10.1186/s12903-025-06104-0
Hamdan MH, Tuzova L, Mol A, et al. The effect of a deep-learning tool on dentists’ performances in detecting apical radiolucencies on periapical radiographs. Dentomaxillofacial Radiology. 2022;51(7):20220122. https://doi.org/10.1259/dmfr.20220122
Mohammad‐Rahimi H, Sohrabniya F, Ourang SA, et al. Artificial intelligence in endodontics: data preparation, clinical applications, ethical considerations, limitations, and future directions. International Endodontic Journal. 2024;57(11):1566-1595. https://doi.org/10.1111/iej.14128
Hamdan M, Yu KW, Miller M, et al. Evaluation of a no-code AI model for detecting periapical radiolucencies: impact of anatomical region on diagnostic performance. BMC Oral Health. 2026;26(1):52. https://doi.org/10.1186/s12903-025-07529-3
Whiting PF, Rutjes AW, Westwood ME, et al. QUADAS-2: a revised tool for the quality assessment of diagnostic accuracy studies. Annals of Internal Medicine. 2011;155(8):529-536. https://doi.org/10.7326/0003-4819-155-8-201110180-00009
Pul U, Tichy A, Pitchika V, et al. Impact of artificial intelligence assistance on diagnosing periapical radiolucencies: A randomized controlled trial. Journal of Dentistry. 2025;160:105868. https://doi.org/10.1016/j.jdent.2025.105868
Çelik B, Savaştaer EF, Kaya HI, et al. The role of deep learning for periapical lesion detection on panoramic radiographs. Dentomaxillofacial Radiology. 2023;52(8):20230118. https://doi.org/10.1259/dmfr.20230118
Ramezanzade S, Laurentiu T, Bakhshandah A, et al. The efficiency of artificial intelligence methods for finding radiographic features in different endodontic treatments-a systematic review. Acta Odontologica Scandinavica. 2023;81(6):422-435. https://doi.org/10.1080/00016357.2022.2158929
Krois J, Garcia Cantu A, Chaurasia A, et al. Generalizability of deep learning models for dental image analysis. Scientific Reports. 2021;11(1):6102. https://doi.org/10.1038/s41598-021-85454-5
Selvaraju RR, Cogswell M, Das A, et al. Grad-cam: Visual explanations from deep networks via gradient-based localization. Proceedings of the IEEE international conference on computer vision; 2017. https://doi.org/10.1007/s11263-019-01228-7
Setzer FC, Shi KJ, Zheng Z, et al. Artificial intelligence for the computer-aided detection of periapical lesions in cone-beam computed tomographic images. Journal of endodontics. 2020;46(7):987-993. https://doi.org/10.1016/j.joen.2020.03.025
Fryback DG, Thornbury JR. The efficacy of diagnostic imaging. Medical Decision Making. 1991;11(2):88-94. https://doi.org/10.1177/0272989X9101100203
Cotti E, Schirru E. Present status and future directions: Imaging techniques for the detection of periapical lesions. International Endodontic Journal. 2022;55:1085-1099. https://doi.org/10.1111/iej.13828
Mongan J, Moy L, Kahn Jr CE. Checklist for artificial intelligence in medical imaging (CLAIM): a guide for authors and reviewers. Radiological Society of North America; 2020. p. e200029. https://doi.org/10.1148/ryai.2020200029
Sounderajah V, Ashrafian H, Golub RM, et al. Developing a reporting guideline for artificial intelligence-centred diagnostic test accuracy studies: the STARD-AI protocol. BMJ Open. 2021;11(6):e047709. https://doi.org/10.1136/bmjopen-2020-047709
Endres MG, Hillen F, Salloumis M, et al. Development of a deep learning algorithm for periapical disease detection in dental radiographs. Diagnostics. 2020;10(6):430. https://doi.org/10.3390/diagnostics10060430
