Dental caries is the most prevalent dental disease worldwide, and neural networks and artificial intelligence are increasingly being used in the field of dentistry. This systematic review aims to identify the state of the art of neural networks in caries detection and diagnosis. A search was conducted in PubMed, Institute of Electrical and Electronics Engineers (IEEE) Xplore, and ScienceDirect. Data extraction was performed independently by two reviewers. The quality of the selected studies was assessed using the Cochrane Handbook tool. Thirteen studies were included. Most of the included studies employed periapical, near-infrared light transillumination, and bitewing radiography. The image databases ranged from 87 to 3000 images, with a mean of 669 images. Seven of the included studies labeled the dental caries in each image by experienced dentists. Not all of the studies detailed how caries was defined, and not all detailed the type of carious lesion detected. Each study included in this review used a different neural network and different outcome metrics. All this variability complicates the conclusions that can be made about the reliability or not of a neural network to detect and diagnose caries. A comparison between neural network and dentist results is also necessary.

Bone plays an important role in dental implant treatment success. The goal of this literature review is to analyze the influence of bone definition and finite element parameters on stress in dental implants and bone in numerical studies. A search was conducted of Pubmed, Science Direct and LILACS, and two independent reviewers performed the data extraction. The quality of the selected studies was assessed using the Cochrane Handbook tool for clinical trials. Seventeen studies were included. Titanium was the most commonly-used material in dental implants. The magnitude of the applied loads varied from 15 to 300 N with a mean of 182 N. Complete osseointegration was the most common boundary condition. Evidence from this review suggests that bone is commonly defined as an isotropic material, despite being an anisotropic tissue, and that it is analyzed as a ductile material, instead of as a fragile material. In addition, and in view of the data analyzed in this review, it can be concluded that there is no standardization for conducting finite element studies in the field of dentistry. Convergence criteria are only detailed in two of the studies included in this review, although they are a key factor in obtaining accurate results in numerical studies. It is therefore necessary to implement a methodology that indicates which parameters a numerical simulation must include, as well as how the results should be analyzed.

Neural networks are increasingly being used in the field of dentistry. The aim of this literature review was to visualize the state of the art of artificial intelligence in dental applications, such as the detection of teeth, caries, filled teeth, crown, prosthesis, dental implants and endodontic treatment. A search was conducted in PubMed, the Institute of Electrical and Electronics Engineers (IEEE) Xplore and arXiv.org. Data extraction was performed independently by two reviewers. Eighteen studies were included. The variable teeth was the most analyzed (n = 9), followed by caries (n = 7). No studies detecting dental implants and filled teeth were found. Only two studies investigated endodontic applications. Panoramic radiographies were the most common image employed (n = 5), followed by periapical images (n = 3). Near-infrared light transillumination images were employed in two studies and bitewing and computed tomography (CT) were employed in one study. The included articles used a wide variety of neuronal networks to detect the described variables. In addition, the database used also had a great heterogeneity in the number of images. A standardized methodology should be used in order to increase the compatibility and robustness between studies because of the heterogeneity in the image database, type, neural architecture and results.

Sergio Alexandre Gehrke, DDS, PhD/Rafael Arcesio Delgado-Ruiz, DDS, PhD/Juan Carlos Prados Frutos, DDS, PhD/Mar?a Prados-Privado, PhD/Berenice Anina Dedavid, PhD/Jose Manuel Granero Mar?n, DDS, PhD/Jos? Luiz Calvo Guirado, DDS, PhD: Purpose: This study aimed to evaluate the misfit of three different implant-abutment connections before and after cycling load. Materials and Methods: One hundred twenty dental implants and correspondent prefabricated titanium abutments were used. Three different implant-abutment connections were evaluated: Morse taper (MT group), external hexagon (EH group), and internal hexagon (IH group). Forty implants and 40 abutments were used per group. The parameters for the mechanical evaluation were set as: 360,000 cycles, load of 150 N, and frequency of 4 Hz. Samples were sectioned in their longitudinal and transversal axes, and the misfit of the implant-abutment connection was evaluated by scanning electron microscopy analysis. One-way analyses of variance, Tukey post hoc analyses (α = .05), and t test (P < .05) were used to determine differences between groups. Results: At the longitudinal direction, all the groups showed the presence of microgaps before cycling load; after cycling load, microgaps were reduced in all groups (P > .05). Transversally, only the MT group showed full fitting after cycling load compared with the other groups (EH and IH) (P < .0001). Conclusion: The application of cycling load produces an accommodation of the implant-abutment connection in internal, external, and Morse taper connections. In the longitudinal direction, the accommodation decreases and/or eliminates the gap observed initially (before load). In the horizontal direction, Morse cone implant-abutment connections experience a complete accommodation with the elimination of the gap.