World Journal of Dentistry

Register      Login

VOLUME 11 , ISSUE 2 ( March-April, 2020 ) > List of Articles

ORIGINAL RESEARCH

Are Artificial Neural Networks Useful for Predicting Overhanging Dental Restorations? A Cross-sectional Study

Hani T Fadel, Osama Abu-Hammad, Omar A Ghulam, Najla Dar-Odeh

Keywords : Algorithms, Dental restoration failure, Neural networks, Overhanging dental restoration, Prediction

Citation Information : Fadel HT, Abu-Hammad O, Ghulam OA, Dar-Odeh N. Are Artificial Neural Networks Useful for Predicting Overhanging Dental Restorations? A Cross-sectional Study. World J Dent 2020; 11 (2):99-104.

DOI: 10.5005/jp-journals-10015-1709

License: CC BY-NC 4.0

Published Online: 01-09-2020

Copyright Statement:  Copyright © 2020; The Author(s).


Abstract

Aims: To predict the number of overhanging dental restorations (ODRs) using an artificial neural network (ANN) and determine the most important predictive variables. Materials and methods: Patient- and restoration-related data were used as input variables to construct two networks, with (network 1) and without (network 2) the number of secondary caries lesions (SCLs) as input data. Output data were the number of ODRs. Of the 502 participants, data of the first 100 were used to build/train the model. Those of the remaining 402 were used to test the model for prediction accuracy. Results: Model accuracy notably increased after training. Prediction of ODRs was more accurate in network 1. Allowing for an error of ±1, network 1 predicted the number of ODRs with an accuracy of 85.6%, whereas that of network 2 was only 82.1% accurate. The number of old fillings was the most important input variable, while gender was the least important. Conclusion: Within the study limits, the ANN model predicted ODRs with more than 85% accuracy. The number of old fillings was the most important predictive variable. Clinical significance: Making use of ANN analyses can help periodontists and general dentists predict the occurrence of ODRs, formulate effective treatment planning, and reduce patient discomfort and unnecessary costs.


HTML PDF Share
  1. Jokstad A, Bayne S, Blunck U, et al. Quality of dental restorations. FDI Commission Project 2-95. Int Dent J 2001;51(3):117–158. DOI: 10.1002/j.1875-595x.2001.tb00832.x.
  2. Selwitz RH, Ismail AI, Pitts NB. Dental caries. Lancet 2007;369(9555): 51–59. DOI: 10.1016/S0140-6736(07)60031-2.
  3. Fernandes N, Vally Z, Sykes L. The longevity of restorations -A literature review. South African Dent J 2015;70:410–413.
  4. Donovan TE. Longevity of the tooth/restoration complex: a review. J California Dent Assoc 2006;34(2):122–128.
  5. Sirajuddin S, Narasappa KM, Gundapaneni V, et al. Iatrogenic damage to periodontium by restorative treatment procedures: an overview. Open Dent J 2015;9(1):217–222. DOI: 10.2174/1874210601509010217.
  6. Brunsvold MA, Lane JJ. The prevalence of overhanging dental restorations and their relationship to periodontal disease. J Clin Periodontol 1990;17(2):67–72. DOI: 10.1111/j.1600-051x.1990.tb01064.x.
  7. Winter C, Payet JP, Suttle CA. Modeling the winter-to-summer transition of prokaryotic and viral abundance in the arctic ocean. PLoS ONE 2012;7(12):e52794-e. DOI: 10.1371/journal.pone. 0052794.
  8. Gargouri N, Dammak Masmoudi A, Sellami Masmoudi D, et al. A new GLLD operator for mass detection in digital mammograms. Int J Biomed Imag 2012;2012:765649. DOI: 10.1155/2012/765649.
  9. Kim EY, Lim KO, Rhee HS. Predictive modeling of dental pain using neural network. Stud Health Technol Informat 2009;146:745–746.
  10. Dar-Odeh NS, Alsmadi OM, Bakri F, et al. Predicting recurrent aphthous ulceration using genetic algorithms-optimized neural networks. Adv Appl Bioinform Chem 2010;3:7–13. DOI: 10.2147/aabc.s10177.
  11. Al Haidan A, Abu-Hammad O, Dar-Odeh N. Predicting tooth surface loss using genetic algorithms-optimized artificial neural networks. Computation Mathemat Methods Med 2014;2014:106236. DOI: 10.1155/2014/106236.
  12. Fadel H, Al Hamdan K, Rhbeini Y, et al. Root caries and risk profiles using the cariogram in different periodontal disease severity groups. Acta Odontol Scand 2011;69(2):118–124. DOI: 10.3109/00016357.2010.538718.
  13. Lino JR, Ramos-Jorge J, Coelho VS, et al. Association and comparison between visual inspection and bitewing radiography for the detection of recurrent dental caries under restorations. Int Dent J 2015;65(4):178–181. DOI: 10.1111/idj.12172.
  14. Pythia - The Neural Network Designer. USA: Runtime Software 2000.
  15. Park WJ, Park J-B. History and application of artificial neural networks in dentistry. Europ J Dent 2018;12(4):594–601. DOI: 10.4103/ejd.ejd_325_18.
  16. Sarkar NK. Creep, corrosion and marginal fracture of dental amalgams. J Oral Rehabilitat 1978;5(4):413–423. DOI: 10.1111/j.1365-2842.1978.tb01260.x.
  17. Hervas-Garcia A, Martinez-Lozano MA, Cabanes-Vila J, et al. Composite resins. A review of the materials and clinical indications. Med Oral Patol Oral Cir Bucal 2006;11(2):E215–E220.
  18. Herman K, Czajczyńska-Waszkiewicz A, Kowalczyk-Zając M, et al. Assessment of the influence of vegetarian diet on the occurrence of erosive and abrasive cavities in hard tooth tissues. Postepy Hig Med Dosw (Online) 2011;65:764–769. DOI: 10.5604/17322693.967066.
  19. Staufenbiel I, Adam K, Deac A, et al. Influence of fruit consumption and fluoride application on the prevalence of caries and erosion in vegetarians--a controlled clinical trial. Europ J Clin Nutri 2015;69(10):1156–1160. DOI: 10.1038/ejcn.2015.20.
  20. Moradi-Lakeh M, El Bcheraoui C, Afshin A, et al. Diet in Saudi Arabia: findings from a nationally representative survey. Pub Heal Nutri 2017;20(6):1075–1081. DOI: 10.1017/S1368980016003141.
  21. Kirsch J, Tchorz J, Hellwig E, et al. Decision criteria for replacement of fillings: a retrospective study. Clin Experiment Dent Res 2016;2(2):121–128. DOI: 10.1002/cre2.30.
  22. Ghulam OA, Fadel HT. Can clusters based on caries experience and medical status explain the distribution of overhanging dental restorations and recurrent caries? A cross-sectional study in Madinah - Saudi Arabia. Saudi J Biolog Sci 2018;25(2):367–371. DOI: 10.1016/j.sjbs.2017.02.001.
  23. Xie X, Wang L, Wang A. Artificial neural network modeling for deciding if extractions are necessary prior to orthodontic treatment. Angle Orthod 2010;80(2):262–266. DOI: 10.2319/111608-588.1.
PDF Share
PDF Share

© Jaypee Brothers Medical Publishers (P) LTD.