Advertisement

Critical Analysis of Artificial Intelligence in Endodontics: A Scoping Review

Published:November 24, 2021DOI:https://doi.org/10.1016/j.joen.2021.11.007

      Abstract

      Introduction

      Artificial intelligence (AI) comprises computational models that mimic the human brain to perform various diagnostic tasks in clinical practice. The aim of this scoping review was to systematically analyze the AI algorithms and models used in endodontics and identify the source quality and type of evidence.

      Methods

      A literature search was conducted in October 2020 to identify the relevant literature in English language in the 4 major health sciences databases, ie, MEDLINE, Dentistry & Oral Science, CINAHL Plus, and Cochrane Library. Our review questions were the following: what are the different AI algorithms and models used in endodontics?, what are the datasets being used?, what type of performance metrics were reported?, and what diagnostic performance measures were used?. The quality of the included studies was evaluated by a modified Quality Assessment of Studies of Diagnostic Accuracy risk (QUADAS) tool.

      Results

      Out of 300 studies, 12 articles met our inclusion criteria and were subjected to final analysis. Among the included studies, 6 studies focused on periapical pathology, and 3 studies investigated vertical root fractures. Most studies (n = 10) used neural networks, among which convolutional neural networks were commonly used. The datasets that were mostly studied were radiographs. Out of 12 studies, only 3 studies achieved a high score according to the modified QUADAS tool.

      Conclusions

      AI models had acceptable performance, ie, accuracy >90% in executing various diagnostic tasks. The scientific reporting of AI-related research is irregular. The endodontic community needs to implement recommended guidelines to improve the weaknesses in the current planning and reporting of AI-related research to improve its scientific vigor.

      Key Words

      To read this article in full you will need to make a payment

      Purchase one-time access:

      Academic & Personal: 24 hour online accessCorporate R&D Professionals: 24 hour online access
      One-time access price info
      • For academic or personal research use, select 'Academic and Personal'
      • For corporate R&D use, select 'Corporate R&D Professionals'

      Subscribe:

      Subscribe to Journal of Endodontics
      Already a print subscriber? Claim online access
      Already an online subscriber? Sign in
      Institutional Access: Sign in to ScienceDirect

      References

        • Kaul V.
        • Enslin S.
        • Gross S.A.
        The history of artificial intelligence in medicine.
        Gastrointest Endosc. 2020; 92: 807-812
        • Mahmood H.
        • Shaban M.
        • Indave B.
        • et al.
        Use of artificial intelligence in diagnosis of head and neck precancerous and cancerous lesions: a systematic review.
        Oral Oncol. 2020; 110: 104885
        • Montavon G.
        • Samek W.
        • Müller K.-R.
        Methods for interpreting and understanding deep neural networks.
        Digit Signal Process. 2018; 73: 1-15
        • Schwendicke F.
        • Golla T.
        • Dreher M.
        • et al.
        Convolutional neural networks for dental image diagnostics: a scoping review.
        J Dent. 2019; 91: 103226
        • Khanagar S.B.
        • Al-Ehaideb A.
        • Maganur P.C.
        • et al.
        Developments, application, and performance of artificial intelligence in dentistry: a systematic review.
        J Dent Sci. 2021; 16: 508-522
        • Tran B.X.
        • Vu G.T.
        • Ha G.H.
        • et al.
        Global evolution of research in artificial intelligence in health and medicine: a bibliometric study.
        J Clin Med. 2019; 8: 360
        • Umer F.
        • Khan M.
        A call to action: concerns related to artificial intelligence.
        Oral Surg Oral Med Oral Pathol Oral Radiol. 2021; 132: 255
        • Khalil H.
        • Peters M.D.
        • Tricco A.C.
        • et al.
        Conducting high quality scoping reviews: challenges and solutions.
        J Clin Epidemiol. 2021; 130: 156-160
        • Peters M.D.
        • Marnie C.
        • Tricco A.C.
        • et al.
        Updated methodological guidance for the conduct of scoping reviews.
        JBI Evid Synth. 2020; 18: 2119-2126
        • Whiting P.F.
        • Rutjes A.W.
        • Westwood M.E.
        • et al.
        QUADAS-2: a revised tool for the quality assessment of diagnostic accuracy studies.
        Ann Intern Med. 2011; 155: 529-536
        • Kositbowornchai S.
        • Plermkamon S.
        • Tangkosol T.
        Performance of an artificial neural network for vertical root fracture detection: an ex vivo study.
        Dent Traumatol. 2013; 29: 151-155
        • Saghiri M.A.
        • Asgar K.
        • Boukani K.K.
        • et al.
        A new approach for locating the minor apical foramen using an artificial neural network.
        Int Endod J. 2012; 45: 257-265
        • Saghiri M.A.
        • Garcia-Godoy F.
        • Gutmann J.L.
        • et al.
        The reliability of artificial neural network in locating minor apical foramen: a cadaver study.
        J Endod. 2012; 38: 1130-1134
        • Birdal R.
        • Gumus E.
        • Sertbas A.
        • et al.
        Automated lesion detection in panoramic dental radiographs.
        Oral Radiol. 2016; 32: 111-118
        • Ekert T.
        • Krois J.
        • Meinhold L.
        • et al.
        Deep learning for the radiographic detection of apical lesions.
        J Endod. 2019; 45: 917-922.e5
        • Fukuda M.
        • Inamoto K.
        • Shibata N.
        • et al.
        Evaluation of an artificial intelligence system for detecting vertical root fracture on panoramic radiography.
        Oral Radiol. 2020; 36: 337-343
        • 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 (20180218)
        • 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 (20160107)
        • Okada K.
        • Rysavy S.
        • Flores A.
        • et al.
        Noninvasive differential diagnosis of dental periapical lesions in cone-beam CT scans.
        Med Phys. 2015; 42: 1653-1665
        • Orhan K.
        • Bayrakdar I.S.
        • Ezhov M.
        • et al.
        Evaluation of artificial intelligence for detecting periapical pathosis on cone-beam computed tomography scans.
        Int Endod J. 2020; 53: 680-689
        • Setzer F.C.
        • Shi K.J.
        • Zhang Z.
        • et al.
        Artificial intelligence for the computer-aided detection of periapical lesions in cone-beam computed tomographic images.
        J Endod. 2020; 46: 987-993
        • Endres M.G.
        • Hillen F.
        • Salloumis M.
        • et al.
        Development of a deep learning algorithm for periapical disease detection in dental radiographs.
        Diagnostics (Basel). 2020; 10: 430
        • Carin L.
        • Pencina M.J.
        On deep learning for medical image analysis.
        JAMA. 2018; 320: 1192-1193
        • Yamashita R.
        • Nishio M.
        • Do R.K.G.
        • et al.
        Convolutional neural networks: an overview and application in radiology.
        Insights Imaging. 2018; 9: 611-629
        • Greenspan H.
        • Van Ginneken B.
        • Summers R.M.
        Guest editorial: deep learning in medical imaging—overview and future promise of an exciting new technique.
        IEEE Trans Med Imaging. 2016; 35: 1153-1159
        • Wang F.
        • Casalino L.P.
        • Khullar D.
        Deep learning in medicine: promise, progress, and challenges.
        JAMA Intern Med. 2019; 179: 293-294
        • Handelman G.S.
        • Kok H.K.
        • Chandra R.V.
        • et al.
        Peering into the black box of artificial intelligence: evaluation metrics of machine learning methods.
        AJR Am J Roentgenol. 2019; 212: 38-43
        • Abdalla-Aslan R.
        • Yeshua T.
        • Kabla D.
        • et al.
        An artificial intelligence system using machine-learning for automatic detection and classification of dental restorations in panoramic radiography.
        Oral Surg Oral Med Oral Pathol Oral Radiol. 2020; 130: 593-602
        • Wang S.
        • Summers R.M.
        Machine learning and radiology.
        Med Image Anal. 2012; 16: 933-951
        • Hossin M.
        • Sulaiman M.
        A review on evaluation metrics for data classification evaluations.
        Int J Data Min Knowl Manag Process. 2015; 5: 1
        • Pethani F.
        Promises and perils of artificial intelligence in dentistry.
        Aust Dent J. 2021; 66: 124-135
        • Aminoshariae A.
        • Kulild J.
        • Nagendrababu V.
        Artificial intelligence in endodontics: current applications and future directions.
        J Endod. 2021; 47: 1352-1357
        • Talwar S.
        • Utneja S.
        • Nawal R.R.
        • et al.
        Role of cone-beam computed tomography in diagnosis of vertical root fractures: a systematic review and meta-analysis.
        J Endod. 2016; 42: 12-24
        • Pratten D.H.
        • McDonald N.J.
        Comparison of radiographic and electronic working lengths.
        J Endod. 1996; 22: 173-176
        • Tewary S.
        • Luzzo J.
        • Hartwell G.
        Endodontic radiography: who is reading the digital radiograph?.
        J Endod. 2011; 37: 919-921
        • Neelakantan P.
        • Subbarao C.
        • Subbarao C.V.
        Comparative evaluation of modified canal staining and clearing technique, cone-beam computed tomography, peripheral quantitative computed tomography, spiral computed tomography, and plain and contrast medium-enhanced digital radiography in studying root canal morphology.
        J Endod. 2010; 36: 1547-1551
        • Habib S.
        • Umer F.
        Comments on “Artificial intelligence applications in restorative dentistry: a systematic review”.
        J Prosthet Dent. 2021; https://doi.org/10.1016/j.prosdent.2021.08.003
        • Revilla-Leon M.
        • Gomez-Polo M.
        • Vyas S.
        • et al.
        Artificial intelligence applications in restorative dentistry: a systematic review.
        J Prosthet Dent. 2021; https://doi.org/10.1016/j.prosdent.2021.02.010
        • Mongan J.
        • Moy L.
        • Kahn Jr., C.E.
        Checklist for Artificial Intelligence in Medical Imaging (CLAIM): a guide for authors and reviewers.
        Radiol Artif Intell. 2020; 2: e200029
        • Schwendicke F.
        • Singh T.
        • Lee J.H.
        • et al.
        IADR e-oral health network and the ITU WHO focus group AI for Health: artificial intelligence in dental research—checklist for authors, reviewers, readers.
        J Dent. 2021; 107: 103610