Materials and Methods: In order to conduct this review, the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines were followed. Studies published from 2015 to 2021 under the keywords (deep convolutional neural network) AND (caries), (deep learning caries) AND (convolutional neural network) AND (caries) were systematically reviewed.

Results: When dental caries is improperly diagnosed, the lesion may eventually invade the enamel, dentin, and pulp tissue, leading to loss of tooth function. Rapid and precise detection and diagnosis are vital for implementing appropriate prevention and treatment of dental caries. Radiography and intraoral images are considered to play a vital role in detecting dental caries; nevertheless, studies have shown that 20% of suspicious areas are mistakenly diagnosed as dental caries using this technique; hence, diagnosis via radiography alone without an objective assessment is inaccurate. Identifying caries with a deep convolutional neural network-based detector enables the operator to distinguish changes in the location and morphological features of dental caries lesions. Deep learning algorithms have broader and more profound layers and are continually being developed, remarkably enhancing their precision in detecting and segmenting objects.

AIM: This systematic review aimed to investigate the performance of AI systems in identifying dental anomalies in paediatric dentistry and compare it with human performance.

DESIGN: A systematic search of Scopus, PubMed and Google Scholar was conducted from 2012 to 2022. Inclusion criteria were based on problem/patient/population, intervention/indicator, comparison and outcome scheme and specific keywords related to AI, DL, paediatric dentistry, dental anomalies, supernumerary and mesiodens. Six of 3918 initial pool articles were included, assessing nine DL sub-systems that used panoramic radiographs or cone-beam computed tomography. Article quality was assessed using QUADAS-2.

RESULTS: Artificial intelligence systems based on DL algorithms showed promising potential in enhancing the speed and accuracy of dental anomaly detection, with an average of 85.38% accuracy and 86.61% sensitivity. Human performance, however, outperformed AI systems, achieving 95% accuracy and 99% sensitivity. Limitations included a limited number of articles and data heterogeneity.

METHODS: PMMA disks containing GO (0.01, 0.05, 0.1, or 0.5 wt%) were subjected to a biaxial flexural test to determine the Weibull parameters (m: modulus of Weibull; σ0: characteristic strength; n = 30 at 1 MPa/s) and slow crack growth (SCG) parameters (n: subcritical crack growth susceptibility coefficient, σf0: scaling parameter; n = 10 at 10-2, 10-1, 101, 100 and 102 MPa/s). Strength-probability-time (SPT) diagrams were plotted by merging SCG and Weibull parameters.

RESULTS: There was no significant difference in the m value of all materials. However, 0.5 GO presented the lowest σ0, whereas all other groups were similar. The lowest n value obtained for all GO-modified PMMA groups (27.4 for 0.05 GO) was higher than the Control (15.6). The strength degradation predicted after 15 years for Control was 12%, followed by 0.01 GO (7%), 0.05 GO (9%), 0.1 GO (5%), and 0.5 GO (1%).