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Neural network approach to evaluate the physical properties of dentin

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Abstract

This study intended to evaluate the effects of inorganic trace elements such as magnesium (Mg), strontium (Sr), and zinc (Zn) on root canal dentin using an Artificial Neural Network (ANN). The authors obtained three hundred extracted human premolars from type II diabetic individuals and divided them into three groups according to the solutions used (Mg, Sr, or Zn). The authors subdivided the specimens for each experimental group into five subgroups according to the duration for which the authors soaked the teeth in the solution: 0 (control group), 1, 2, 5, and 10 min (n = 20). The authors then tested the specimens for root fracture resistance (RFR), surface microhardness (SμH), and tubular density (TD). The authors used the data obtained from half of the specimens in each subgroup (10 specimens) for the training of ANN. The authors then used the trained ANN to evaluate the remaining data. The authors analyzed the data by Kolmogorov–Smirnov, one-way ANOVA, post hoc Tukey, and linear regression analysis (P < 0.05). Treatment with Mg, Sr, and Zn significantly increased the values of RFR and SμH (P < 0.05), and decreased the values of TD in dentin specimens (P < 0.05). The authors did not notice any significant differences between evaluations by manual or ANN methods (P > 0.05). The authors concluded that Mg, Sr, and Zn may improve the RFR and SμH, and decrease the TD of root canal dentin in diabetic individuals. ANN may be used as a reliable method to evaluate the physical properties of dentin.

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Acknowledgements

MAS is a recipient of the New Jersey Health Foundation, TechAdvance, and DenburTech Awards. This publication is dedicated to the memory of Dr. H. Afsar Lajevardi [46], a legendary Pediatrician (1953–2015). The authors will never forget Dr. H Afsar Lajevardi's kindness and support. The views expressed in this paper are those of the authors and do not necessarily reflect the views or policies of the affiliated organizations. The authors hereby announce that they had active cooperation in this scientific study and preparation of the present article. The authors confirm that they have no financial involvement with any commercial company or organization with direct financial interest regarding the materials used in this study. Special thanks to Anna Vakhnovetsky for proofreading and Dr. Fatereh Samadi for interpreting the results in this research. The authors deny any conflicts of interest related to this study.

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Authors and Affiliations

  1. Biomaterial and Prosthodontics Laboratory, Department of Restorative Dentistry, Rutgers School of Dental Medicine, 185 South Orange Avenue, Newark, NJ, 07103, USA

    Mohammad Ali Saghiri

  2. Department of Endodontics, University of the Pacific, Arthur A. Dugoni School of Dentistry, San Francisco, CA, USA

    Mohammad Ali Saghiri

  3. Sector of Angiogenesis Regenerative Medicine, Dr. Hajar Afsar Lajevardi Research Cluster (DHAL), Hackensack, NJ, USA

    Ali Mohammad Saghiri, Elham Samadi & Julia Vakhnovetsky

  4. Biomaterial Research Laboratory, Department of Restorative Dentistry, Rutgers School of Dental Medicine, Newark, NJ, USA

    Devyani Nath

  5. Rutgers School of Dental Medicine, Newark, NJ, USA

    Julia Vakhnovetsky

  6. Department of Restorative Dentistry, Rutgers School of Dental Medicine, Newark, NJ, USA

    Steven M. Morgano

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  1. Mohammad Ali Saghiri
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Saghiri, M.A., Saghiri, A.M., Samadi, E. et al. Neural network approach to evaluate the physical properties of dentin. Odontology 111, 68–77 (2023). https://doi.org/10.1007/s10266-022-00726-4

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