Caries Detection from Dental Images using Novel Maximum Directional Pattern (MDP) and Deep Learning

Abstract

Various machine learning technologies and artificial intelligence techniques were applied on different applications of dentistry. Caries detection in orthodontics is a very much needed process. Computer-aided diagnosis (CAD) method is used to detect caries in dental radiographs. The feature extraction and classification are involved in the process of caries detection in dental images. In the 2D images the geometric feature extraction methods are applied and the features are extracted and then applied to machine learning algorithms for classification. Different feature extraction techniques can also be combined and then the fused features can be used for classification. Different classifiers support vector machine (SVM), deep learning, decision tree classifier (DT), Naïve Bayes (NB) classifier, k-nearest neighbor classifier (KNN) and random forest (RF) classifier can be used for the classification process. The proposed MDP extracts both intensity and edge information and creates the feature vector that increases the classification accuracy during caries detection.

AI and Face-Driven Orthodontics: A Scoping Review of Digital Advances in Diagnosis and Treatment Planning

“…A substantial amount of studies focused either on assessing the quality of obtained images or even on identifying CT, MRI scans and X-rays that showed no pathologies [52,53]. On the other hand, AI techniques can also detect pathological processes, e.g., dental caries on radiographs, with an increasing level of accuracy [54].…”