Application of deep learning and feature selection technique on external root resorption identification on CBCT images.

BACKGROUND: Artificial intelligence has been proven to improve the identification of various maxillofacial lesions. The aim of the current study is two-fold: to assess the performance of four deep learning models (DLM) in external root resorption (ERR) identification and to assess the effect of combining feature selection technique (FST) with DLM on their ability in ERR identification. METHODS: External root resorption was simulated on 88 extracted premolar teeth using tungsten bur in different depths (0.5 mm, 1 mm, and 2 mm). All teeth were scanned using a Cone beam CT (Carestream Dental, Atlanta, GA). Afterward, a training (70%), validation (10%), and test (20%) dataset were established. The performance of four DLMs including Random Forest (RF) + Visual Geometry Group 16 (VGG), RF + EfficienNetB4 (EFNET), Support Vector Machine (SVM) + VGG, and SVM + EFNET) and four hybrid models (DLM + FST: (i) FS + RF + VGG, (ii) FS + RF + EFNET, (iii) FS + SVM + VGG and (iv) FS + SVM + EFNET) was compared. Five performance parameters were assessed: classification accuracy, F1-score, precision, specificity, and error rate. FST algorithms (Boruta and Recursive Feature Selection) were combined with the DLMs to assess their performance. RESULTS: RF + VGG exhibited the highest performance in identifying ERR, followed by the other tested models. Similarly, FST combined with RF + VGG outperformed other models with classification accuracy, F1-score, precision, and specificity of 81.9%, weighted accuracy of 83%, and area under the curve (AUC) of 96%. Kruskal Wallis test revealed a significant difference (p = 0.008) in the prediction accuracy among the eight DLMs. CONCLUSION: In general, all DLMs have similar performance on ERR identification. However, the performance can be improved by combining FST with DLMs.

Bioactivity and remineralization potential of modified glass ionomer cement: A systematic review of the impact of calcium and phosphate ion release.

This systematic review investigates the effectiveness of calcium and phosphate ions release on the bioactivity and remineralization potential of glass ionomer cement (GIC). Electronic databases, including PubMed-MEDLINE, Scopus, and Web of Science, were systematically searched according to PRISMA guidelines. This review was registered in the PROSPERO database. Five eligible studies on modifying GIC with calcium and phosphate ions were included. The risk of bias was assessed using the RoBDEMAT tool. The incorporation of these ions into GIC enhanced its bioactivity and remineralization properties. It promoted hydroxyapatite formation, which is crucial for remineralization, increased pH and inhibited cariogenic bacteria growth. This finding has implications for the development of more effective dental materials. This can contribute to improved oral health outcomes and the management of dental caries, addressing a prevalent and costly oral health issue. Nevertheless, comprehensive longitudinal investigations are needed to evaluate the clinical efficacy of this GIC's modification.