Longevity Prediction of Root Canal Treatment using CNN-Logistic regression model
Keywords:
Root Canal Treatment Failure, Toot Longevity Prediction, Overfilling, Under Filling, Perforation, Root Resorption, Deep Learning, CNN, Logistic Regression, Fusion Approach.Abstract
The root canal treatment can provide long-term relief and preserve the tooth. In general, root canal treatment has a high success rate and can last for many years, often preserving the tooth for a lifetime. However, it should be noted that no dental treatment can guarantee a permanent solution. The longevity of root canal treatment might vary depending on several factors, including the clinical and non-clinical factors such as overall health of the tooth, the quality of the treatment performed, and the oral hygiene practices of the patient along with overfilling, underfilling perforation, or root Resorption.
Therefore, the purpose of this research is to forecast how long a root canal treatment will remain by identifying the factors that are responsible for the treatment failure. The system makes use of textual and image datasets along with logistic regression and CNN in order to find the non-clinical and clinical factors which can more likely to result in treatment failure. Moreover, the fusion of logistic regression and CNN also helps to predict the longevity of the treatment with a higher accuracy of 94.16%.
