Home Dental Radiology Place of a new radiological index in predicting pulp exposure before intervention for deep carious lesions

Place of a new radiological index in predicting pulp exposure before intervention for deep carious lesions

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  • 1.

    Peres MA, Macpherson LMD, Weyant RJ, Daly B, Venturelli R, Mathur MR, et al. Oral diseases: a global public health challenge. Lancet. 2019;394:249–60.

    PubMed 

    Google Scholar
     

  • 2.

    Kassebaum NJ, Bernabé E, Dahiya M, Bhandari B, Murray CJL, Marcenes W. Global burden of untreated caries: a systematic review and metaregression. J Dent Res. 2015;94(5):650–8.

    PubMed 

    Google Scholar
     

  • 3.

    Doméjean S, Grosgogeat B. Evidence-Based Deep Carious Lesion Management: From Concept to Application in Everyday Clinical Practice. Monogr Oral Sci. 2018;27:137–45.


    Google Scholar
     

  • 4.

    Barros MMAF, De Rodrigues MIQ, Muniz FWMG, Rodrigues LKA. Selective, stepwise, or nonselective removal of carious tissue: which technique offers lower risk for the treatment of dental caries in permanent teeth? A systematic review and meta-analysis. Clin Oral Invest. 2020;24:521–32.


    Google Scholar
     

  • 5.

    Villat C, Attal J-P, Brulat N, Decup F, Doméjean S, Dursun E, et al. One-step partial or complete caries removal and bonding with antibacterial or traditional self-etch adhesives: study protocol for a randomized controlled trial. Trials. 2016;17:404.

    PubMed 
    PubMed Central 

    Google Scholar
     

  • 6.

    Bjørndal L, Simon S, Tomson PL, Duncan HF. Management of deep caries and the exposed pulp. Int Endod J. 2019;52:949–73.

    PubMed 

    Google Scholar
     

  • 7.

    Schwendicke F, Stolpe M, Meyer-Lueckel H, Paris S, Dörfer CE. Cost-effectiveness of one- and two-step incomplete and complete excavations. J Dent Res. 2013;92:880–7.

    PubMed 

    Google Scholar
     

  • 8.

    Braga MM, Mendes FM, Ekstrand KR. Detection activity assessment and diagnosis of dental caries lesions. Dent Clin North Am. 2010;54:479–93.

    PubMed 

    Google Scholar
     

  • 9.

    Neuhaus KW, Lussi A. Carious lesion diagnosis: methods, problems, thresholds. In: Schwendicke F, Frencken J, Innes N, editors. Monographs in oral science. Karger; 2018. p. 24–31 (cité 8 Mars 2020) https://www.karger.com/Article/FullText/487828.


    Google Scholar
     

  • 10.

    Wenzel A. Radiographic display of carious lesions and cavitation in approximal surfaces: advantages and drawbacks of conventional and advanced modalities. Acta Odontol Scand. 2014;72:251–64.

    PubMed 

    Google Scholar
     

  • 11.

    Schwendicke F, Splieth C, Breschi L, Banerjee A, Fontana M, Paris S, et al. When to intervene in the caries process? An expert Delphi consensus statement. Clin Oral Invest. 2019;23:3691–703.


    Google Scholar
     

  • 12.

    Innes NP, Frencken JE, Bjørndal L, Maltz M, Manton DJ, Ricketts D, Van Landuyt K, Banerjee A, Campus G, Doméjean S, Fontana M, Leal S, Lo E, Machiulskiene V, Schulte A, Splieth C, Zandona A, Schwendicke F. Managing Carious Lesions: consensus recommendations on terminology. Adv Dent Res. 2016;28(2):49–57.

    PubMed 

    Google Scholar
     

  • 13.

    Bland JM, Altman DG. Statistical methods for assessing agreement between two methods of clinical measurement. Lancet. 1986;1:307–10. https://doi.org/10.1016/S0140-6736(86)90837-8.


    Google Scholar
     

  • 14.

    Lin L, Hedayat AS, Wu W. Statistical tools for measuring agreement. Springer Science & Business Media; 2012. p. 161.


    Google Scholar
     

  • 15.

    Yu Y, Lin L (2012) Agreement: statistical tools for measuring agreement. R package version 0.8-1. Available from https://cran.r-project.org/src/contrib/Archive/Agreement/. Accessed 14 Oct 2020

  • 16.

    Carroll RJ, Ruppert D, Stefanski LA, Crainiceanu CM. Measurement error in nonlinear models: a modern perspective. 2nd ed. Chapman & Hall/CRC; 2006. p. 484.


    Google Scholar
     

  • 17.

    Asparouhov T, Muthén B. Structural equation models and mixture models with continuous nonnormal skewed distributions. Structural equation modeling: a multidisciplinary journal, vol. 23. Routledge; 2016. p. 1–19. https://doi.org/10.1080/10705511.2014.947375.


    Google Scholar
     

  • 18.

    R Core Team (2019) R: a language and environment for statistical computing. R Foundation for statistical computing, Vienna, Austria. Available from https://www.R-project.org/. Accessed 14 Oct 2020

  • 19.

    Schwendicke F, Tzschoppe M, Paris S. Radiographic caries detection: a systematic review and meta-analysis. J Dent. 2015;43:924–33.

    PubMed 

    Google Scholar
     

  • 20.

    Berbari R, Khairallah A, Kazan HF, Ezzedine M, Bandon D, Sfeir E. Measurement reliability of the remaining dentin thickness below deep carious lesions in primary molars. Int J Clin Pediatr Dent. 2018;11:23–8.

    PubMed 

    Google Scholar
     

  • 21.

    Jhany NA, Hawaj BA, Hassan AA, Semrani ZA, Bulowey MA, Ansari S. Comparison of the Estimated Radiographic Remaining Dentine Thickness with the Actual Thickness Below the Deep Carious Lesions on the Posterior Teeth: An in vitro Study. Eur Endod J. 2019;4(3):139–44.


    Google Scholar
     

  • 22.

    Lancaster PE, Craddock HL, Carmichael FA. Estimation of remaining dentine thickness below deep lesions of caries. Br Dent J. 2011;211:E20–E20.

    PubMed 

    Google Scholar
     

  • 23.

    Shokri A, Kasraei S, Lari S, Mahmoodzadeh M, Khaleghi A, Musavi S, et al. Efficacy of denoising and enhancement filters for detection of approximal and occlusal caries on digital intraoral radiographs. J Conserv Dent. 2018;21:162.

    PubMed 
    PubMed Central 

    Google Scholar
     

  • 24.

    Nascimento EH, Gaêta-Araujo H, Vasconcelos KF, Freire BB, Oliveira-Santos C, Haiter-Neto F, et al. Influence of brightness and contrast adjustments on the diagnosis of proximal caries lesions. Dentomaxillofac Radiol. 2018;47:20180100.

    PubMed 
    PubMed Central 

    Google Scholar
     

  • 25.

    Haak R, Wicht MJ, Noack MJ. Conventional, digital and contrast-enhanced bitewing radiographs in the decision to restore approximal carious lesions. Caries Res. 2001;35:193–9.

    PubMed 

    Google Scholar
     

  • 26.

    Belém MDF, Ambrosano GMB, Tabchoury CPM, Ferreira-Santos RI, Haiter-Neto F. Performance of digital radiography with enhancement filters for the diagnosis of proximal caries. Braz Oral Res. 2013;27:245–51.

    PubMed 

    Google Scholar
     

  • 27.

    Kajan ZD, Davalloo RT, Tavangar M, Valizade F. The effects of noise reduction, sharpening, enhancement, and image magnification on diagnostic accuracy of a photostimulable phosphor system in the detection of non-cavitated approximal dental caries. Imaging Sci Dent. 2015;45:81.

    PubMed 
    PubMed Central 

    Google Scholar
     

  • 28.

    Møystad A, Svanaes DB, Risnes S, Larheim TA, Gröndahl HG. Detection of approximal caries with a storage phosphor system. A comparison of enhanced digital images with dental X-ray film. Dentomaxillofac Radiol. 1996;25:202–6.

    PubMed 

    Google Scholar
     

  • 29.

    Seneadza V, Koob A, Kaltschmitt J, Staehle HJ, Duwenhoegger J, Eickholz P. Digital enhancement of radiographs for assessment of interproximal dental caries. Dentomaxillofac Radiol. 2008;37:142–8.

    PubMed 

    Google Scholar
     

  • 30.

    Gaêta-Araujo H, Nascimento EHL, Brasil DM, Gomes AF, Freitas DQ, De Oliveira-Santos C. Detection of Simulated Periapical Lesion in Intraoral Digital Radiography with Different Brightness and Contrast. Eur Endod J. 2019;4(3):133–38.

    PubMed 
    PubMed Central 

    Google Scholar
     

  • 31.

    Leksell E, Ridell K, Cvek M, Mejare I. Pulp exposure after stepwise versus direct complete excavation of deep carious lesions in young posterior permanent teeth. Dent Traumatol. 1996;12:192–6.


    Google Scholar
     

  • 32.

    Li MD, Arun NT, Gidwani M, Chang K, Deng F, Little BP, et al. Automated assessment and tracking of COVID-19 pulmonary disease severity on chest radiographs using convolutional Siamese neural networks. Radiol Artif Intell. 2020;2:e200079.

    PubMed 
    PubMed Central 

    Google Scholar
     

  • 33.

    Ishioka J, Matsuoka Y, Uehara S, Yasuda Y, Kijima T, Yoshida S, et al. Computer-aided diagnosis of prostate cancer on magnetic resonance imaging using a convolutional neural network algorithm. BJU Int. 2018;122:411–7.

    PubMed 

    Google Scholar
     

  • 34.

    Abdolell M, Tsuruda K, Schaller G, Caines J. Statistical evaluation of a fully automated mammographic breast density algorithm. Comput Math Methods Med. 2013;2013:1–6.


    Google Scholar
     

  • 35.

    Grischke J, Johannsmeier L, Eich L, Griga L, Haddadin S. Dentronics: towards robotics and artificial intelligence in dentistry. Dent Mater. 2020;36:765–78.

    PubMed 

    Google Scholar
     

  • 36.

    Schwendicke F, Samek W, Krois J. Artificial intelligence in dentistry: chances and challenges. J Dent Res. 2020;99:769–74.

    PubMed 
    PubMed Central 

    Google Scholar
     



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