Evaluation of the trueness and precision of conventional impressions versus digital scans for the all-on-four treatment in the maxillary arch: An in vitro study.

PURPOSE: To compare the accuracy of digitizing conventional impressions to intraoral surface scans for all-on-four treatment in the maxillary arch. MATERIALS AND METHODS: An edentulous maxillary arch model with four implants placed in an all-on-four design was fabricated. Intraoral surface scans (n = 10) were obtained using an intraoral scanner after scan body insertion. For conventional polyvinylsiloxane impressions of the model, implant copings were inserted into the implant fixation for implant level, opened tray impressions (n = 10). The model and conventional impressions were digitized to obtain digital files. A reference file was created using a laboratory-scanned conventional standard tessellation language (STL) file with analog to scan the body using exocad software. STL datasets from the two digital and conventional impression groups were superimposed with reference files to assess the 3D deviations. Two-way ANOVA and paired-samples t-test was performed to assess the difference in trueness and examine the effects of impression technique and implant angulation on the deviation amount. RESULTS: No significant differences were found between the conventional impression and intraoral surface scan groups F(1, 76) = 2.705, p = 0.104. No significant differences were found between conventional straight and digital straight implants and between conventional and digital tilted implants F(1, 76) = .041, p = 0.841. No significant differences were found between conventional straight and conventional tilted implants p = 0.07 and between digital straight and digital tilted implants p = 0.08. CONCLUSION: Digital scans were more accurate than conventional impressions. The digital straight implants were more accurate than the conventional straight implants, and the digital tilted implants were more accurate than the conventional tilted implants, with higher accuracy for digital straight implants.

Artificial intelligence in the detection and classification of dental caries.

STATEMENT OF PROBLEM: Automated detection of dental caries could enhance early detection, save clinician time, and enrich treatment decisions. However, a reliable system is lacking. PURPOSE: The purpose of this study was to train a deep learning model and to assess its ability to detect and classify dental caries. MATERIAL AND METHODS: Bitewings radiographs with a 1876×1402-pixel resolution were collected, segmented, and anonymized with a radiographic image analysis software program and were identified and classified according to the modified King Abdulaziz University (KAU) classification for dental caries. The method was based on supervised learning algorithms trained on semantic segmentation tasks. RESULTS: The mean score for the intersection-over-union of the model was 0.55 for proximal carious lesions on a 5-category segmentation assignment and a mean F1 score of 0.535 using 554 training samples. CONCLUSIONS: The study validated the high potential for developing an accurate caries detection model that will expedite caries identification, assess clinician decision-making, and improve the quality of patient care.

Maxillary and mandibular complete-arch implant rehabilitation using a complete digital workflow: A case report.

This article summarizes the clinical steps involved in maxillary and mandibular complete-arch implant rehabilitation using a complete digital workflow. The maxillary arch was recorded using the double digital scan technique, and the mandibular arch using the triple digital scan technique. The digital protocol used in this case report allowed the recording of implant positions via scan bodies, soft tissues, and, most importantly, the interocclusal relationship in the same visit. A new technique for mandibular digital scan was described that uses soft tissue landmarks by creating windows in the patient's provisional prostheses to superimpose the three digital scans and consequently fabricate and verify the maxillary and mandibular prototype prostheses and definitive complete-arch zirconia prostheses.

The Quality of AI-Generated Dental Caries Multiple Choice Questions: A Comparative Analysis of ChatGPT and Google Bard Language Models.

Statement of problem: AI technology presents a variety of benefits and challenges for educators. Purpose: To investigate whether ChatGPT and Google Bard (now is named Gemini) are valuable resources for generating multiple-choice questions for educators of dental caries. Material and methods: A book on dental caries was used. Sixteen paragraphs were extracted by an expert consultant based on applicability and potential for developing multiple-choice questions. ChatGPT and Bard language models were used to produce multiple-choice questions based on this input, and 64 questions were generated. Three dental specialists assessed the relevance, accuracy, and complexity of the generated questions. The questions were qualitatively evaluated using cognitive learning objectives and item writing flaws. Paired sample t-tests and two-way analysis of variance (ANOVA) were used to compare the generated multiple-choice questions and answers between ChatGPT and Bard. Results: There were no significant differences between the questions generated by ChatGPT and Bard. Moreover, the analysis of variance found no significant differences in question quality. Bard-generated questions tended to have higher cognitive levels than those of ChatGPT. Format error was predominant in ChatGPT-generated questions. Finally, Bard exhibited more absolute terms than ChatGPT. Conclusions: ChatGPT and Bard could generate questions related to dental caries, mainly at the cognitive level of knowledge and comprehension. Clinical significance: Language models are crucial for generating subject-specific questions used in quizzes, tests, and education. By using these models, educators can save time and focus on lesson preparation and student engagement instead of solely focusing on assessment creation. Additionally, language models are adept at generating numerous questions, making them particularly valuable for large-scale exams. However, educators must carefully review and adapt the questions to ensure they align with their learning goals.

Mapping the Landscape of the Digital Workflow of Esthetic Veneers from Design to Cementation: A Systematic Review.

The purpose of this systematic review was to map all the existing literature on digitally designed and fabricated esthetic veneers. We aimed to compare the accuracy of digitally designed preparation and cementation guides for esthetic indirect veneers with the conventional workflow. We evaluated studies comparing the accuracy and predictability of workflows between digitally fabricated indirect-esthetic veneers and conventional indirect veneers. Searches were performed in August 2023 across three databases, specifically Google Scholar, Cochrane, and PubMed, and were restricted to English-language publications. The search strategy was based on the PICO criteria. Reference lists of identified articles were manually checked to find further pertinent studies that were not discovered during the electronic search. The titles and abstracts were reviewed in the first stage, and then the full article texts were reviewed and cross-matched against the predetermined inclusion criteria. Following the search, 169 articles were identified: 41 from Google Scholar, 44 from Cochrane, and 71 from PubMed, with 13 added manually. Of these, 20 were chosen for a detailed quality assessment of the digital veneer workflow and the accuracy of digital preparations and cementation guides for laminate veneers. Based on our findings, the digitally fabricated laminate-veneer workflow demonstrated superior predictability and accuracy compared to the conventional workflow.

Artificial intelligence (AI) in restorative dentistry: Performance of AI models designed for detection of interproximal carious lesions on primary and permanent dentition.

Objective: The objective of this study was to evaluate the effectiveness of deep learning methods in detecting dental caries from radiographic images. Methods: A total of 771 bitewing radiographs were divided into two groups: adult (n = 554) and pediatric (n = 217). Two distinct semantic segmentation models were constructed for each group. They were manually labeled by general dentists for semantic segmentation. The inter-examiner reliability of the two examiners was also measured. Finally, the models were trained using transfer learning methodology along with computer science advanced tools, such as ensemble U-Nets with ResNet50, ResNext101, and Vgg19 as the encoders, which were all pretrained on ImageNet weights using a training dataset. Results: Intersection over union (IoU) score was used to evaluate the outcomes of the deep learning model. For the adult dataset, the IoU averaged 98%, 23%, 19%, and 51% for zero, primary, moderate, and advanced carious lesions, respectively. For pediatric bitewings, the IoU averaged 97%, 8%, 17%, and 25% for zero, primary, moderate, and advanced caries, respectively. Advanced caries was more accurately detected than primary caries on adults and pediatric bitewings P < 0.05. Conclusions: The proposed deep learning models can accurately detect advanced caries in permanent or primary bitewing radiographs. Misclassification mostly occurs between primary and moderate caries. Although the model performed well in correctly classifying the lesions, it can misclassify one as the other or does not accurately capture the depth of the lesion at this early stage.

Mechanical Properties of Three-Dimensional Printed Provisional Resin Materials for Crown and Fixed Dental Prosthesis: A Systematic Review.

The emergence of digital dentistry has led to the introduction of various three-dimensional (3D) printing materials in the market, specifically for provisional fixed restoration. This study aimed to undertake a systematic review of the published literature on the Mechanical Properties of 3D- Printed Provisional Resin Materials for crown and fixed dental prosthesis (FDP). The electronic database on PubMed/Medline was searched for relevant studies. The search retrieved articles that were published from January 2011 to March 2023. The established focus question was: "Do provisional 3D-printed materials have better mechanical properties than conventional or milled provisional materials?". The systematically extracted data included the researcher's name(s), publication year, evaluation method, number of samples, types of materials, and study outcome. A total of 19 studies were included in this systematic review. These studies examined different aspects of the mechanical properties of 3D-printed provisional materials. Flexural Strength and Microhardness were the frequently used mechanical testing. Furthermore, 3D-printed provisional restorations showed higher hardness, smoother surfaces, less wear volume loss, and higher wear resistance compared to either milled or conventional, or both. 3D-printed provisional resin materials appear to be a promising option for fabricating provisional crowns and FDPs.