Development of artificial neural network model for prediction of post-streptococcus mutans in dental caries

Syed Javed; M Zakirulla; Rahmath Ulla Baig; S M Asif; Allah Baksh Meer

doi:10.1016/j.cmpb.2019.105198

Development of artificial neural network model for prediction of post-streptococcus mutans in dental caries

Comput Methods Programs Biomed. 2020 Apr:186:105198. doi: 10.1016/j.cmpb.2019.105198. Epub 2019 Nov 12.

Authors

Syed Javed¹, M Zakirulla², Rahmath Ulla Baig³, S M Asif⁴, Allah Baksh Meer⁵

Affiliations

¹ Mechancial Engineering Department, College of Engineering, King Khalid University, Abha, Saudi Arabia. Electronic address: syedjavedme@gmail.com.
² Department of Pediatric Dentistry and Orthodontic Sciences, College of Dentistry, King Khalid University, Abha, Saudi Arabia.
³ Industrial Engineering Department, College of Engineering, King Khalid University, Abha, Saudi Arabia.
⁴ Department of Diagnostic Science & Oral Biology, College of Dentistry, King Khalid University, Abha, Saudi Arabia.
⁵ Department of Public Health, College of Health Sciences, Saudi Electronic University, Jeddah, Saudi Arabia.

PMID: 31760304
DOI: 10.1016/j.cmpb.2019.105198

Abstract

Background and objective: Streptococcus mutans is the primary initiator and most common organism associated with dental caries. Prediction of post-Streptococcus mutans favours in the selection of appropriate caries excavation method which eventually results in meliorate caries-free cavity preparation for restoration. The objective of this study is to predict the post-Streptococcus mutans prior to dental caries excavation based on pre- Streptococcus mutans using iOS App developed on Artificial Neural Network (ANN) model.

Methods: For the current research work, children with occlusal dentinal caries lesion were chosen, 45 primary molar teeth cases were studied. Caries excavation was done with carbide bur, polymer bur and spoon excavator. The colony forming units for pre and post-Streptococcus mutans were recorded, data emanating from clinical trials was employed to develop the ANN models. ANN models were trained, validated and tested with the registered clinical data using different ANN architectures.

Results: Feedforward backpropagation ANN model with an architecture of 4-5-1, predicts post-Streptococcus mutans with an efficiency of 0.99033, mean squared error and mean absolute percentage error for testing cases were 0.2341 and 4.967 respectively.

Conclusions: Caries excavation methods and pre-Streptococcus mutans are feed as inputs, while post-Streptococcus mutans as targets to develop ANN model. Based on the developed ANN model, an ingenious iOS App was developed, the global clinician may utilize the App to meticulously predict post-Streptococcus mutans on iPhone based on pre-Streptococcus mutans, which in turn aids in decision making for the selection of caries excavation method. This study manifests the potential application of iOS App with built-in ANN model in efficiently predicting the post-Streptococcus mutans. Also, the study extends scope for applications of iOS App with built-in ANN models in clinical medicine.

Keywords: Artificial neural network; Cross-validation; Dental caries; PSm iOS App; Randomization; Streptococcus mutans.

MeSH terms

Adolescent
Child
Dental Caries / microbiology*
Dental Caries / therapy
Humans
Neural Networks, Computer*
Streptococcus mutans / growth & development*