An evaluation of virtually planned and 3D-printed stereolithographic surgical guides from CBCT and digital scans: An in vitro study

An Overview on Image-Based and Scanner-Based 3D Modeling Technologies

Advances in the scientific fields of photogrammetry and computer vision have led to the development of automated multi-image methods that solve the problem of 3D reconstruction. Simultaneously, 3D scanners have become a common source of data acquisition for 3D modeling of real objects/scenes/human bodies. This article presents a comprehensive overview of different 3D modeling technologies that may be used to generate 3D reconstructions of outer or inner surfaces of different kinds of targets. In this context, it covers the topics of 3D modeling using images via different methods, it provides a detailed classification of 3D scanners by additionally presenting the basic operating principles of each type of scanner, and it discusses the problem of generating 3D models from scans. Finally, it outlines some applications of 3D modeling, beyond well-established topographic ones.

Fabrication of Customized dental guide by stereolithography method and evaluation of dimensional accuracy with artificial neural networks

The present study was investigated the production dental guides by using additive manufacturing stereolithography (SLA) technology, and the dimensional aperture values of the dental guides for dental implant treatment using artificial intelligence technology. The aim of this study is to benefit from artificial neural networks (ANNs) to classify the results obtained from the new production freedom of SLA by changing the existing design concept of dental guides. In the study, the Three Dimensional (3D) anatomical model was designed by using Mimics programme the data obtained from the Cone Beam Computed Tomography (CBCT) images of the patient. Three different dental guide designs were performed using the 3-Matic programme for dental implant treatment on the obtained 3D anatomical model. Dental guide designs and mandible model were produced with a SLA 3D printer, and a data set was created using a 3D scanner. The dimensional aperture values were obtained by performing the 3D registration process between the mandible and dental guides. The data set was analyzed both statistically with Jamovi 2.0.0 software and ANNs. The results showed that the minimum and maximum aperture values obtained from the dental guides were very close to each other, indicating that the guides were compatible with the mandible bone. The statistical results showed that the dimensional aperture values decrease in proportion to the values with minimum arithmetic mean value in the data set, and it was determined that the dental guide-3 was the most suitable model for the mandible. When all test data in the confusion matrix obtained from ten different aritificial neural network models created using ANNs were examined, it was been seen that ANN model-5 was the most successful model with an accuracy rate of 99%.