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

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%.

Influence of Artificial Intelligence-Driven Diagnostic Tools on Treatment Decision-Making in Early Childhood Caries: A Systematic Review of Accuracy and Clinical Outcomes

“…Ngnamsie Njimbouom et al developed a decision support system based on machine learning algorithms to assist in treatment planning for dental caries [ 8 ]. A similar study of the use of AI was also reported on the regarding the detection of fluoride concentration in drinking water in Turkey, and the results showed that the use of AI was cheaper, faster, and more feasible than the use of many chemical analysis techniques available in the laboratory [ 20 ].…”

“…Beyond caries detection, Ngnamsie Njimbouom et al developed a decision support system based on machine learning algorithms to assist with treatment planning for dental caries [ 8 ]. The study revealed that the system significantly improved treatment consistency among dental professionals, highlighting the potential benefits of AI integration into dental practice.…”

Deep Recurrent Neural Network-Based Assessment of Human Dental Age and Gender from Dental Radiographs

“…Several crucial procedures are involved in the initial data preprocessing to guarantee the consistency and usefulness of the secondary dataset [28]. This includes scaling the dental radiography pictures to a joint resolution so that the deep learning model can process them consistently.…”

Predicting Site Energy Usage Intensity Using Machine Learning Models

“…Throughout the years, with the ever-increasing amounts of data, computational models (machine learning-based solutions) have proven effective in addressing a diverse range of practical issues in public healthcare [ 13 , 14 , 15 ], bioinformatics [ 16 ], natural language processing (NLP) [ 17 ], and many others.…”