Objectives: Using computer-aided design and manufacturing (CAD-CAM) technology in restorative dentistry increased the application of lithium disilicate (LD) materials. The bond strength to core and repairing materials is crucial in the restoration's longevity. This systematic review evaluates the shear bond strength (SBS) of CAD-CAM-LD restorative materials to other materials using different surface treatments.
View Article and Find Full Text PDFTo evaluate the accuracy and consistency of chatbots in answering questions related to special needs dentistry. Nine publicly accessible chatbots, including Google Bard, ChatGPT 4, ChatGPT 3.5, Llama, Sage, Claude 2 100k, Claude-instant, Claude-instant-100k, and Google PaLM, were evaluated on their ability to answer a set of 25 true/false questions related to special needs dentistry and 15 questions for syndrome diagnosis based on their oral manifestations.
View Article and Find Full Text PDFBackground: Artificial intelligence (AI)-based tools have shown promise in histopathology image analysis in improving the accuracy of oral squamous cell carcinoma (OSCC) detection with intent to reduce human error.
Objectives: This systematic review and meta-analysis evaluated deep learning (DL) models for OSCC detection on histopathology images by assessing common diagnostic performance evaluation metrics for AI-based medical image analysis studies.
Methods: Diagnostic accuracy studies that used DL models for the analysis of histopathological images of OSCC compared to the reference standard were analyzed.
Objectives: To improve reporting and comparability as well as to reduce bias in dental computer vision studies, we aimed to develop a Core Outcome Measures Set (COMS) for this field. The COMS was derived consensus based as part of the WHO/ITU/WIPO Global Initiative AI for Health (WHO/ITU/WIPO AI4H).
Methods: We first assessed existing guidance documents of diagnostic accuracy studies and conducted interviews with experts in the field.
Background: The objective of this study was to compare the cytotoxicity of TDV and Rebase II denture hard liners on human gingival fibroblasts, aiming to address issues associated with incomplete polymerization and free monomers that affect material properties.
Methods: Seventy-two specimens (24 each of TDV, Rebase II, and controls) were prepared under aseptic conditions according to factory instructions. Cytotoxicity was determined using the MTT test with methyl tetrazolium salt added to the cell culture medium.
Purpose: Smile design software increasingly relies on artificial intelligence (AI). However, using AI for smile design raises numerous technical and ethical concerns. This study aimed to evaluate these ethical issues.
View Article and Find Full Text PDFOral Surg Oral Med Oral Pathol Oral Radiol
May 2024
Objectives: In this study, we assessed 6 different artificial intelligence (AI) chatbots (Bing, GPT-3.5, GPT-4, Google Bard, Claude, Sage) responses to controversial and difficult questions in oral pathology, oral medicine, and oral radiology.
Study Design: The chatbots' answers were evaluated by board-certified specialists using a modified version of the global quality score on a 5-point Likert scale.
Objectives: Artificial Intelligence has applications such as Large Language Models (LLMs), which simulate human-like conversations. The potential of LLMs in healthcare is not fully evaluated. This pilot study assessed the accuracy and consistency of chatbots and clinicians in answering common questions in pediatric dentistry.
View Article and Find Full Text PDFTo systematically evaluate artificial intelligence applications for diagnostic and treatment planning possibilities in pediatric dentistry. PubMed, EMBASE, Scopus, Web of Science, IEEE, medRxiv, arXiv, and Google Scholar were searched using specific search queries. The Quality Assessment of Diagnostic Accuracy Studies-2 (QUADAS-2) checklist was used to assess the risk of bias assessment of the included studies.
View Article and Find Full Text PDFObjective: This study aimed to review and synthesize studies using artificial intelligence (AI) for classifying, detecting, or segmenting oral mucosal lesions on photographs.
Materials And Method: Inclusion criteria were (1) studies employing AI to (2) classify, detect, or segment oral mucosa lesions, (3) on oral photographs of human subjects. Included studies were assessed for risk of bias using Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2).
Objectives: Improved tools based on deep learning can be used to accurately number and identify teeth. This study aims to review the use of deep learning in tooth numbering and identification.
Methods: An electronic search was performed through October 2023 on PubMed, Scopus, Cochrane, Google Scholar, IEEE, arXiv, and medRxiv.
Background: Dentists begin the diagnosis by identifying and enumerating teeth. Panoramic radiographs are widely used for tooth identification due to their large field of view and low exposure dose. The automatic numbering of teeth in panoramic radiographs can assist clinicians in avoiding errors.
View Article and Find Full Text PDFPurpose: This study aims to review deep learning applications for detecting head and neck cancer (HNC) using magnetic resonance imaging (MRI) and radiographic data.
Methods: Through January 2023, a PubMed, Scopus, Embase, Google Scholar, IEEE, and arXiv search were carried out. The inclusion criteria were implementing head and neck medical images (computed tomography (CT), positron emission tomography (PET), MRI, Planar scans, and panoramic X-ray) of human subjects with segmentation, object detection, and classification deep learning models for head and neck cancers.
Objective: Artificial Intelligence (AI) refers to the ability of machines to perform cognitive and intellectual human tasks. In dentistry, AI offers the potential to enhance diagnostic accuracy, improve patient outcomes and streamline workflows. The present study provides a framework and a checklist to evaluate AI applications in dentistry from this perspective.
View Article and Find Full Text PDFMaxillofac Plast Reconstr Surg
March 2023
Artificial intelligence (AI) refers to using technologies to simulate human cognition to solve a specific problem. The rapid development of AI in the health sector has been attributed to the improvement of computing speed, exponential increase in data production, and routine data collection. In this paper, we review the current applications of AI for oral and maxillofacial (OMF) cosmetic surgery to provide surgeons with the fundamental technical elements needed to understand its potential.
View Article and Find Full Text PDFObjectives: Despite deep learning's wide adoption in dental artificial intelligence (AI) research, researchers from other dental fields and, more so, dental professionals may find it challenging to understand and interpret deep learning studies, their employed methods, and outcomes. The objective of this primer is to explain the basic concept of deep learning. It will lay out the commonly used terms, and describe different deep learning approaches, their methods, and outcomes.
View Article and Find Full Text PDFStatement Of Problem: The conventional method of fabricating removable partial denture (RPD) patterns is a time-consuming, expensive, and complex process, and the success of the treatment depends on the fit of the framework. Questions still remain as to whether the 3D-printing method is an acceptable procedure compared with the conventional method.
Purpose: The purpose of this in vitro study was to compare the fit of RPDs cast from 3D printed frameworks and conventionally fabricated RPDs according to the gaps between the framework and the reference model.
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