Diagnostic Test Accuracy of Artificial Intelligence in Detecting Periapical Periodontitis on Two-Dimensional Radiographs: A Retrospective Study and Literature Review

Overview

Journal Medicina (Kaunas)

Publisher MDPI

Specialty General Medicine

Date 2023 Apr 28

PMID 37109726

Authors

Julien Issa

Mouna Jaber

Ismail Rifai

Paul Mozdziak

Bartosz Kempisty

Marta Dyszkiewicz-Konwinska

Affiliations

Soon will be listed here.

Abstract

This study aims to evaluate the diagnostic accuracy of artificial intelligence in detecting apical pathosis on periapical radiographs. A total of twenty anonymized periapical radiographs were retrieved from the database of Poznan University of Medical Sciences. These radiographs displayed a sequence of 60 visible teeth. The evaluation of the radiographs was conducted using two methods (manual and automatic), and the results obtained from each technique were afterward compared. For the ground-truth method, one oral and maxillofacial radiology expert with more than ten years of experience and one trainee in oral and maxillofacial radiology evaluated the radiographs by classifying teeth as healthy and unhealthy. A tooth was considered unhealthy when periapical periodontitis related to this tooth had been detected on the radiograph. At the same time, a tooth was classified as healthy when no periapical radiolucency was detected on the periapical radiographs. Then, the same radiographs were evaluated by artificial intelligence, Diagnocat (Diagnocat Ltd., San Francisco, CA, USA). Diagnocat (Diagnocat Ltd., San Francisco, CA, USA) correctly identified periapical lesions on periapical radiographs with a sensitivity of 92.30% and identified healthy teeth with a specificity of 97.87%. The recorded accuracy and F1 score were 96.66% and 0.92, respectively. The artificial intelligence algorithm misdiagnosed one unhealthy tooth (false negative) and over-diagnosed one healthy tooth (false positive) compared to the ground-truth results. Diagnocat (Diagnocat Ltd., San Francisco, CA, USA) showed an optimum accuracy for detecting periapical periodontitis on periapical radiographs. However, more research is needed to assess the diagnostic accuracy of artificial intelligence-based algorithms in dentistry.

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References

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

Al-Rawi N, Sultan A, Rajai B, Shuaeeb H, Alnajjar M, Alketbi M . The Effectiveness of Artificial Intelligence in Detection of Oral Cancer. Int Dent J. 2022; 72(4):436-447. PMC: 9381387. DOI: 10.1016/j.identj.2022.03.001. View

Arslan Z, Demir H, Berker Yildiz D, Yasar F . Diagnostic accuracy of panoramic radiography and ultrasonography in detecting periapical lesions using periapical radiography as a gold standard. Dentomaxillofac Radiol. 2020; 49(6):20190290. PMC: 7461733. DOI: 10.1259/dmfr.20190290. View

Hamdan M, Tuzova L, Mol A, Tawil P, Tuzoff D, Tyndall D . The effect of a deep-learning tool on dentists' performances in detecting apical radiolucencies on periapical radiographs. Dentomaxillofac Radiol. 2022; 51(7):20220122. PMC: 9522978. DOI: 10.1259/dmfr.20220122. View

Zadrozny L, Regulski P, Brus-Sawczuk K, Czajkowska M, Parkanyi L, Ganz S . Artificial Intelligence Application in Assessment of Panoramic Radiographs. Diagnostics (Basel). 2022; 12(1). PMC: 8774336. DOI: 10.3390/diagnostics12010224. View

Yu V, Messer H, Shen L, Yee R, Stephen Hsu C . Lesion progression in post-treatment persistent endodontic lesions. J Endod. 2012; 38(10):1316-21. DOI: 10.1016/j.joen.2012.06.040. View

Bayrakdar I, Orhan K, Celik O, Bilgir E, Saglam H, Kaplan F . A U-Net Approach to Apical Lesion Segmentation on Panoramic Radiographs. Biomed Res Int. 2022; 2022:7035367. PMC: 8783705. DOI: 10.1155/2022/7035367. View

Moidu N, Sharma S, Chawla A, Kumar V, Logani A . Deep learning for categorization of endodontic lesion based on radiographic periapical index scoring system. Clin Oral Investig. 2021; 26(1):651-658. DOI: 10.1007/s00784-021-04043-y. View

Yamashita R, Nishio M, Do R, Togashi K . Convolutional neural networks: an overview and application in radiology. Insights Imaging. 2018; 9(4):611-629. PMC: 6108980. DOI: 10.1007/s13244-018-0639-9. View

10.

Ekert T, Krois J, Meinhold L, Elhennawy K, Emara R, Golla T . Deep Learning for the Radiographic Detection of Apical Lesions. J Endod. 2019; 45(7):917-922.e5. DOI: 10.1016/j.joen.2019.03.016. View

11.

Forsberg J, Halse A . Radiographic simulation of a periapical lesion comparing the paralleling and the bisecting-angle techniques. Int Endod J. 1994; 27(3):133-8. DOI: 10.1111/j.1365-2591.1994.tb00242.x. View

12.

Issa J, Olszewski R, Dyszkiewicz-Konwinska M . The Effectiveness of Semi-Automated and Fully Automatic Segmentation for Inferior Alveolar Canal Localization on CBCT Scans: A Systematic Review. Int J Environ Res Public Health. 2022; 19(1). PMC: 8744855. DOI: 10.3390/ijerph19010560. View

13.

. Quality guidelines for endodontic treatment: consensus report of the European Society of Endodontology. Int Endod J. 2006; 39(12):921-30. DOI: 10.1111/j.1365-2591.2006.01180.x. View

14.

Orhan K, Bayrakdar I, Ezhov M, Kravtsov A, Ozyurek T . Evaluation of artificial intelligence for detecting periapical pathosis on cone-beam computed tomography scans. Int Endod J. 2020; 53(5):680-689. DOI: 10.1111/iej.13265. View

15.

Schwendicke F, Samek W, Krois J . Artificial Intelligence in Dentistry: Chances and Challenges. J Dent Res. 2020; 99(7):769-774. PMC: 7309354. DOI: 10.1177/0022034520915714. View

16.

Khanagar S, Al-Ehaideb A, Maganur P, Vishwanathaiah S, Patil S, Baeshen H . Developments, application, and performance of artificial intelligence in dentistry - A systematic review. J Dent Sci. 2021; 16(1):508-522. PMC: 7770297. DOI: 10.1016/j.jds.2020.06.019. View

17.

Mohammad-Rahimi H, Motamedian S, Rohban M, Krois J, Uribe S, Mahmoudinia E . Deep learning for caries detection: A systematic review. J Dent. 2022; 122:104115. DOI: 10.1016/j.jdent.2022.104115. View

18.

Karunakaran J, Abraham C, Karthik A, Jayaprakash N . Successful Nonsurgical Management of Periapical Lesions of Endodontic Origin: A Conservative Orthograde Approach. J Pharm Bioallied Sci. 2017; 9(Suppl 1):S246-S251. PMC: 5731023. DOI: 10.4103/jpbs.JPBS_100_17. View

19.

Garcia C, Vera Sempere F, Penarrocha Diago M, Marti Bowen E . The post-endodontic periapical lesion: histologic and etiopathogenic aspects. Med Oral Patol Oral Cir Bucal. 2007; 12(8):E585-90. View

20.

Rohlin M, Kullendorff B, Ahlqwist M, Henrikson C, Hollender L, Stenstrom B . Comparison between panoramic and periapical radiography in the diagnosis of periapical bone lesions. Dentomaxillofac Radiol. 1989; 18(4):151-5. DOI: 10.1259/dmfr.18.4.2640445. View