Ryu JE, et al. J Adv Prosthodont. 2020 Aug;12(4):225-232. https://doi.org/10.4047/jap.2020.12.4.225

In recent years, artificial intelligence (AI) has made remarkable advancements and achieved significant accomplishments across the entire field of dentistry. Notably, efforts to apply AI in prosthodontics are continually progressing. This scoping review aims to present the applications and performance of AI in dental crown prostheses and related topics. We conducted a literature search of PubMed, Scopus, Web of Science, Google Scholar, and IEEE Xplore databases from January 2010 to January 2024. The included articles addressed the application of AI in various aspects of dental crown treatment, including fabrication, assessment, and prognosis. The initial electronic literature search yielded 393 records, which were reduced to 315 after eliminating duplicate references. The application of inclusion criteria led to analysis of 12 eligible publications in the qualitative review. The AI-based applications included in this review were related to detection of dental crown finish line, evaluation of AI-based color matching, evaluation of crown preparation, evaluation of dental crown designed by AI, identification of a dental crown in an intraoral photo, and prediction of debonding probability. AI has the potential to increase efficiency in processes such as fabricating and evaluating dental crowns, with a high level of accuracy reported in most of the analyzed studies. However, a significant number of studies focused on designing crowns using AI-based software, and these studies had a small number of patients and did not always present their algorithms. Standardized protocols for reporting and evaluating AI studies are needed to increase the evidence and effectiveness.

Background: This study aimed to evaluate the accuracy and clinical usability of implant system classification using automated machine learning on a Google Cloud platform.Methods: Four dental implant systems were selected: Osstem TSIII, Osstem USII, Biomet 3i Os-seotite External, and Dentsply Sirona Xive. A total of 4,800 periapical radiographs (1,200 for each implant system) were collected and labeled based on electronic medical records. Regions of interest were manually cropped to 400×800 pixels, and all images were uploaded to Google Cloud storage. Approximately 80% of the images were used for training, 10% for validation, and 10% for testing. Google automated machine learning (AutoML) Vision automatically executed a neural architecture search technology to apply an appropriate algorithm to the uploaded data. A single-label image classification model was trained using AutoML. The performance of the mod-el was evaluated in terms of accuracy, precision, recall, specificity, and F1 score.Results: The accuracy, precision, recall, specificity, and F1 score of the AutoML Vision model were 0.981, 0.963, 0.961, 0.985, and 0.962, respectively. Osstem TSIII had an accuracy of 100%. Osstem USII and 3i Osseotite External were most often confused in the confusion matrix.Conclusion: Deep learning-based AutoML on a cloud platform showed high accuracy in the classification of dental implant systems as a fine-tuned convolutional neural network. Higher-quality images from various implant systems will be required to improve the performance and clinical usability of the model.

Artificial intelligence (AI), particularly deep learning, is increasingly applied in dental prosthetics, offering new approaches to dental crown design. This scoping review aimed to summarize current evidence on AI-assisted crown design, focusing on algorithm types, dataset characteristics, and evaluation methods. A comprehensive search of PubMed, Scopus, Web of Science, and IEEE Xplore was conducted in February 2025, covering studies published between January 2010 and February 2025. Ten studies met the inclusion criteria, of which four developed custom AI models—mainly based on generative adversarial networks—while six evaluated commercially available software. All studies used digitized dental models obtained from scanned stone casts or intraoral scans, and dataset sizes varied widely. Morphological accuracy was the most frequently reported outcome, assessed in six studies, followed by design time and occlusal contact evaluation. While most AI-generated crowns demonstrated clinically acceptable precision, only four studies fabricated physical crowns and none conducted in vivo validation. These findings suggest that AI-assisted crown design holds promise for improving anatomical accuracy and workflow efficiency, but methodological heterogeneity and the lack of clinical validation highlight the need for standardized evaluation protocols and further in vivo studies.

Hyun-Jun Kong, Jin-Yong Yoo, Sang-Ho Eom, Jun-Hyeok Lee. J Dent Rehabil Appl Sci 2022;38:196-203. https://doi.org/10.14368/jdras.2022.38.4.196