Classification of Early Childhood Caries (ECC) Severity Using Spectroscopic Analysis and Artificial Intelligence

Siti Norbaieah Mohd Hashim; Ahmad Hussein Falaki; Rania Hussein  Alashwal; Raja Kamarulzaman Raja Ibrahim; Tian Swee Tan; Maheza Irna Mohamad Salim

doi:10.11113/mjfas.v21n2.3758

Authors

Siti Norbaieah Mohd Hashim Diagnostic Research Group, Health and Wellness Research Alliance, Universiti Teknologi Malaysia, 81310 UTM Johor Bahru, Johor, Malaysia
Ahmad Hussein Falaki Diagnostic Research Group, Health and Wellness Research Alliance, Universiti Teknologi Malaysia, 81310 UTM Johor Bahru, Johor, Malaysia
Rania Hussein Alashwal Diagnostic Research Group, Health and Wellness Research Alliance, Universiti Teknologi Malaysia, 81310 UTM Johor Bahru, Johor, Malaysia.
Raja Kamarulzaman Raja Ibrahim Faculty of Sciences, Universiti Teknologi Malaysia, 81310 UTM Johor Bahru, Johor, Malaysia.
Tian Swee Tan ᶜBioinspired Device and Tissue Engineering Research Group, Department of Biomedical Engineering and Health Sciences, Faculty of Electrical Engineering, Universiti Teknologi Malaysia, Johor Bahru, Johor, Malaysia; ᵈIJN-UTM Cardiovascular Engineering Centre, Institute of Human Centered Engineering, Universiti Teknologi Malaysia, Johor Bahru, Johor, Malaysia
Maheza Irna Mohamad Salim Diagnostic Research Group, Health and Wellness Research Alliance, Universiti Teknologi Malaysia, 81310 UTM Johor Bahru, Johor, Malaysia

DOI:

https://doi.org/10.11113/mjfas.v21n2.3758

Keywords:

Early childhood caries, saliva, artificial neural network, laser-induced breakdown spectroscopy, telehealth.

Abstract

Early childhood caries (ECC) continues to pose a significant challenge for preschoolers worldwide, with a global prevalence of 46.2% in 2020. In the modern world of telehealth prominence, addressing ECC screening efficiently can be achieved through the integration of AI technology and saliva sample analysis. This framework allows parents and teachers to collect saliva samples from the children remotely to reduce the need for in-person healthcare visits. These samples will then be sent to healthcare facilities for analysis and attention will be given to screening results that indicate medium to high caries risk. In this study, a cohort of 104 kindergarten students have voluntarily provided saliva samples, along with additional parameters like pH, viscosity, quantity of saliva and hydration. The saliva was analyzed using the laser-induced breakdown spectroscopy (LIBS) and the results were used for artificial neural network (ANN) development to classify ECC severity. Two ANN models were developed with Model I used multivariate inputs consisting of pH, viscosity, hydration and spectroscopic saliva results whilst model II is developed based on spectroscopic saliva results only. Both ANN models are capable of effectively predicting ECC risk categories. The first model with multivariate inputs achieved performance accuracy of 91.8% whilst the second model, which relies solely on saliva spectroscopic data, exhibited performance accuracy of 92.7%. This study concluded that the use of telehealth and modern technologies such as LIBS and AI for ECC screening is helpful in ensuring a more accessible and efficient healthcare for young children by revolutionizing the healthcare approach.

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