Effectiveness of Artificial Intelligence Applications Designed for Endodontic Diagnosis, Decision-making, and Prediction of Prognosis: A Systematic Review
Citation Information :
Boreak N. Effectiveness of Artificial Intelligence Applications Designed for Endodontic Diagnosis, Decision-making, and Prediction of Prognosis: A Systematic Review. J Contemp Dent Pract 2020; 21 (8):926-934.
Aim: With advancements in science and technology, there has been phenomenal developments in the application of neural networks in dentistry. This systematic review aimed to report on the effectiveness of artificial intelligence (AI) applications designed for endodontic diagnosis, decision-making, and prediction of prognosis. Materials and methods: Studies reporting on AI applications in endodontics were identified from the electronic databases such as PubMed, Medline, Embase, Cochrane, Google Scholar, Scopus, and Web of Science, for original research articles published from January 1, 2000, to June 1, 2020. A total of 10 studies that met our eligibility criteria were further analyzed for qualitative data. QUADAS-2 was applied for synthesis of the quality of the studies included. Results: A wide range of AI applications have been implemented in endodontics. The neural networks employed were mostly based on convolutional neural networks (CNNs) and artificial neural networks (ANNs) in their neural architectures. These AI models have been used for locating apical foramen, retreatment predictions, prediction of periapical pathologies, detection and diagnosis of vertical root fractures, and assessment of root morphologies. Conclusion: These studies suggest that the neural networks performed similar to the experienced professionals in terms of accuracy and precision. In some studies, these models have even outperformed the specialists. Clinical significance: These models can be of greater assistance as an expert opinion for less experienced and nonspecialists.
Ng Y-L, Mann V, Rahbaran S, et al. Outcome of primary root canal treatment: systematic review of the literature—part 1. Effects of study characteristics on probability of success. Int Endodontic J 2007;40(12):921–939. DOI: 10.1111/j.1365-2591.2007.01322.x.
Eriksen HM, Kirkevang L-L, Petersson K. Endodontic epidemiology and treatment outcome: general considerations. Endodontic Topics 2002;2(1):1–9. DOI: 10.1034/j.1601-1546.2002.20101.x.
Brickley MR, Shepherd JP, Armstrong RA. Neural networks: a new technique for development of decision support systems in dentistry. J Dent 1998;26(4):305–309. DOI: 10.1016/S0300-5712(97)00027-4.
Tripathy M, Maheshwari RP, Verma HK. Power transformer differential protection based on optimal probabilistic neural network. IEEE Trans Power Del 2010;25:102–112. DOI: 10.1109/TPWRD.2009. 2028800.
Lee JH, Kim DH, Jeong SN, et al. Detection and diagnosis of dental caries using a deep learning-based convolutional neural network algorithm. J Dent 2018;77:106–111. DOI: 10.1016/j.jdent.2018. 07.015.
Lee JH, Kim DH, Jeong SN, et al. 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. DOI: 10.5051/jpis.2018.48.2.114.
Casalegno F, Newton T, Daher R, et al. Caries detection with near-infrared transillumination using deep learning. J Dent Res 2019;98:1227–1233. DOI: 10.1177/0022034519871884.
Kise Y, Ikeda H, Fujii T, et al. Preliminary study on the application of deep learning system to diagnosis of Sjögren's syndrome on CT images. Dentomaxillofac Radiol 2019;48(6):20190019. DOI: 10.1259/dmfr.20190019.
Zhang W, Li J, Li Z, et al. Predicting postoperative facial swelling following impacted mandibular third molars extraction by using artificial neural networks evaluation. Sci Rep 2018;8(1):12281. DOI: 10.1038/s41598-018-29934-1.
Choi HI, Jung SK, Baek SH, et al. Artificial intelligent model with neural network machine learning for the diagnosis of orthognathic surgery. J Craniofac Surg 2019;30(7):1986–1989. DOI: 10.1097/SCS.0000000000005650.
Patcas R, Timofte R, Volokitin A, et al. Facial attractiveness of cleft patients: a direct comparison between artificial-intelligence-based scoring and conventional rater groups. Eur J Orthod 2019;41(4):428–433. DOI: 10.1093/ejo/cjz007.
McGrath TA, Alabousi M, Skidmore B, et al. Recommendations for reporting of systematic reviews and meta-analyses of diagnostic test accuracy: a systematic review. Syst Rev 2017;6(1):194. DOI: 10.1186/s13643-017-0590-8.
Whiting PF, Rutjes AWS, Westwood ME, et al. QUADAS-2: a revised tool for the quality assessment of diagnostic accuracy studies. Ann Intern Med 2011;155(8):529–536. DOI: 10.7326/0003-4819-155-8-201110180-00009.
Saghiri MA, Garcia-Godoy F, Gutmann JL, et al. The reliability of artificial neural network in locating minor apical foramen: a cadaver study. J Endod 2012;38(8):1130–1134. DOI: 10.1016/j.joen.2012. 05.004.
Saghiri MA, Asgar K, Boukani KK, et al. A new approach for locating the minor apical foramen using an artificial neural network. Int Endod J 2012;45(3):257–265. DOI: 10.1111/j.1365-2591.2011.01970.x.
Campo L, Aliaga IJ, De Paz JF, et al. Retreatment predictions in odontology by means of CBR systems. Comput Intell Neurosci 2016;2016:7485250. DOI: 10.1155/2016/7485250.
Mahmoud YE, Labib SS, Hoda MO, Mokhtar Clinical Prediction of Teeth Periapical Lesion based on Machine Learning Techniques – An Experimental Study. Proceedings of Second International Conference on Digital Information Processing, Data Mining, and Wireless Communications (DIPDMWC2015), Dubai, UAE, 2015.
Ekert T, Krois J, Meinhold L, et al. 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.
Orhan K, Bayrakdar IS, Ezhov M, et al. 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.
Johari M, Esmaeili F, Andalib A, et al. Detection of vertical root fractures in intact and endodontically treated premolar teeth by designing a probabilistic neural network: an ex vivo study. Dentomaxillofac Radiol 2017;46(2):20160107. DOI: 10.1259/dmfr.20160107.
Fukuda M, Inamoto K, Shibata N, et al. Evaluation of an artificial intelligence system for detecting vertical root fracture on panoramic radiography. Oral Radiol 2019. DOI: 10.1007/s11282-019-00409-x.
Hatvani J, Andras H, Jérôme M, et al. Deep learning-based super-resolution applied to dental computed tomography. IEEE Trans Rad Plasma Med Sci 2019;3(2):120–128. DOI: 10.1109/TRPMS.2018.2827239. ISSN 2469-7311.
Hiraiwa T, Ariji Y, Fukuda M, et al. A deep-learning artificial intelligence system for assessment of root morphology of the mandibular first molar on panoramic radiography. Dentomaxillofac Radiol 2019;48(3):20180218. DOI: 10.1259/dmfr.20180218.
Caputo BV, Noro Filho GA, de Andrade Salgado DM, et al. Evaluation of the root canal morphology of molars by using cone-beam computed tomography in a Brazilian population: part I. J Endod 2016;42(11):1604–1607. DOI: 10.1016/j.joen.2016.07.026.
Seidberg B, Alibrandi B, Fine H, et al. Clinical investigation of measuring working lengths of root canals with an electronic device and with digital-tactile sense. J Am Dent Assoc 1975;90(2):379–387. DOI: 10.14219/jada.archive.1975.0059.
Powell-Cullingford AW, Pitt Ford TR. The use of E-speed film for root canal length determination. Int Endod J 1993;26(5):268–272. DOI: 10.1111/j.1365-2591.1993.tb00571.x.
Gutmann JL, Leonard JE. Problem solving in endodontic working-length determination. Compend Contin Educ Dent 1995;16(3):288–290.
Gordon MP, Chandler NP. Electronic apex locators. Int Endod J 2004;37(7):425–437. DOI: 10.1111/j.1365-2591.2004.00835.x.
Janner SF, Jeger FB, Lussi A, et al. Precision of endodontic working length measurements: a pilot investigation comparing cone-beam computed tomography scanning with standard measurement techniques. J Endod 2011;37(8):1046–1051. DOI: 10.1016/j.joen.2011.05.005.
Llena-Puy MC, Forner-Navarro L, Barbero-Navarro I. Vertical root fracture in endodontically treated teeth: a review of 25 cases. Oral Surg Oral Med Oral Pathol Oral Radiol Endod 2001;92(5):553–555. DOI: 10.1067/moe.2001.117262.
Mora MA, Mol A, Tyndall DA, et al. In vitro assessment of local computed tomography for the detection of longitudinal tooth fractures. Oral Surg Oral Med Oral Pathol Oral Radiol Endod 2007;103(6):825–829. DOI: 10.1016/j.tripleo.2006.09.009.
Tsesis I, Rosen E, Tamse A, et al. Diagnosis of vertical root fractures in endodontically treated teeth based on clinical and radiographic indices: a systematic review. J Endod 2010;36(9):1455–1458. DOI: 10.1016/j.joen.2010.05.003.
Felsypremila G, Vinothkumar TS, Kandaswamy D. Anatomic symmetry of root and root canal morphology of posterior teeth in indian subpopulation using cone beam computed tomography: a retrospective study. Eur J Dent 2015;9(4):500–507. DOI: 10.4103/1305-7456.172623.
Celikten B, Tufenkci P, Aksoy U, et al. Cone beam CT evaluation of mandibular molar root canal morphology in a Turkish cypriot population. Clin Oral Investig 2016;20(8):2221–2226. DOI: 10.1007/s00784-016-1742-2.
Zhang X, Xiong S, Ma Y, et al. A Conebeam computed tomographic study on mandibular first molars in a Chinese subpopulation. PLoS One 2015;10(8):e0134919. DOI: 10.1371/journal.pone.0134919.