MMDCP: Multi-Modal Dental Caries Prediction for Decision Support System Using Deep Learning

Soualihou Ngnamsie Njimbouom, Kwonwoo Lee and Jeong-Dong Kim ()
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Soualihou Ngnamsie Njimbouom: Department of Computer and Electronics Convergence Engineering, Sun Moon University, Asan 31460, Korea
Kwonwoo Lee: Department of Computer and Electronics Convergence Engineering, Sun Moon University, Asan 31460, Korea
Jeong-Dong Kim: Department of Computer and Electronics Convergence Engineering, Sun Moon University, Asan 31460, Korea

IJERPH, 2022, vol. 19, issue 17, 1-16

Abstract: In recent years, healthcare has gained unprecedented attention from researchers in the field of Human health science and technology. Oral health, a subdomain of healthcare described as being very complex, is threatened by diseases like dental caries, gum disease, oral cancer, etc. The critical point is to propose an identification mechanism to prevent the population from being affected by these diseases. The large amount of online data allows scholars to perform tremendous research on health conditions, specifically oral health. Regardless of the high-performing dental consultation tools available in current healthcare, computer-based technology has shown the ability to complete some tasks in less time and cost less than when using similar healthcare tools to perform the same type of work. Machine learning has displayed a wide variety of advantages in oral healthcare, such as predicting dental caries in the population. Compared to the standard dental caries prediction previously proposed, this work emphasizes the importance of using multiple data sources, referred to as multi-modality, to extract more features and obtain accurate performances. The proposed prediction model constructed using multi-modal data demonstrated promising performances with an accuracy of 90%, F1-score of 89%, a recall of 90%, and a precision of 89%.

Keywords: convolutional neural network; artificial neural network; multi-modalities; hybrid neural network; dental caries (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2022
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