Artificial Intelligence in the Study of Root and Canal Anatomy: A Comprehensive Review on Applications, Advantages, Challenges and Future Directions

Focus issue: Artificial intelligence in medical physics.

Symmetry of root anatomy and root canal morphology in maxillary premolars analyzed using cone-beam computed tomography

ObjectivesThis meta-research study explored the availability of artificial intelligence (AI) models from development studies published in leading radiology journals in 2022, with availability defined as the transparent reporting of relevant technical details, such as model architecture and weights, necessary for independent replication.Materials and methodsA systematic search of Ovid Medline and Embase was conducted to identify AI model development studies published in five leading radiology journals in 2022. Data were extracted on study characteristics, model details, and code and model-sharing practices. The proportion of AI studies sharing their models was analyzed. Logistic regression analyses were employed to explore associations between study characteristics and model availability.ResultsOf 268 studies reviewed, 39.9% (n = 107) made their models available. Deep learning (DL) models exhibited particularly low availability, with only 11.5% (n = 13) of the 113 studies being fully available. Training codes for DL models were provided in 22.1% (n = 25), suggesting limited ability to train DL models with one’s own data. Multivariable logistic regression analysis showed that the use of traditional regression-based models (odds ratio [OR], 17.11; 95% CI: 5.52, 53.05; p < 0.001) was associated with higher availability, while the radiomics package usage (OR, 0.27; 95% CI: 0.11, 0.65; p = 0.003) was associated with lower availability.ConclusionThe availability of AI models in radiology publications remains suboptimal, especially for DL models. Enforcing model-sharing policies, enhancing external validation platforms, addressing commercial restrictions, and providing demos for commercial models in open repositories are necessary to improve transparency and replicability in radiology AI research.Key PointsQuestionThe study addresses the limited availability of AI models in radiology, especially DL models, which impacts external validation and clinical reliability.FindingsOnly 39.9% of radiology AI studies made their models available, with DL models showing particularly low availability at 11.5%.Clinical relevanceImproving the availability of radiology AI models is essential for enabling external validation, ensuring reliable clinical application, and advancing patient care by fostering robust and transparent AI systems.Graphical

Advancements in three-dimensional (3D) surface imaging and artificial intelligence (AI) are transforming dental research and clinical practice by providing high-precision, non-invasive tools for diagnosis, treatment planning, and outcome prediction. Traditional imaging methods, while effective, often lack dimensional detail and involve radiation exposure, whereas 3D imaging and AI offer improved safety and accuracy. This systematic review aims to evaluate the efficacy, safety, and practical applications of 3D surface imaging and AI technologies in dentistry, with a focus on orthodontics, maxillofacial surgery, and diagnostic practices.Following PRISMA guidelines, a comprehensive literature search was conducted across databases including PubMed, Cochrane Library, and Google Scholar. Studies were selected based on criteria such as population, intervention type, and outcome relevance. Data extraction and quality assessment were performed using standardized tools, and bias was evaluated with the Cochrane Risk of Bias Tool for randomized controlled trials and ROBINS-I for non-randomized studies.The review included 50 studies encompassing various imaging technologies (e.g., structured light scanning, laser scanning) and AI applications (e.g., convolutional neural networks). Findings indicate significant improvements in diagnostic accuracy, patient-specific modeling, and clinical workflow efficiency. Benefits include reduced radiation exposure, enhanced diagnostic precision, and increased affordability, although challenges remain in terms of operational complexity, cost, and potential AI biases.3D surface imaging and AI represent substantial advancements in dental practice, enabling precise diagnostics, tailored treatment, and improved patient outcomes. Future research should focus on refining AI algorithms, standardizing protocols, and developing accessible, portable 3D imaging devices to expand these technologies’ reach in clinical settings.

Introduction: Artificial intelligence (AI) tools are increasingly being integrated into computer-aided diagnosis systems that can be applied to improve the recognition and clinical and molecular characterization of allergic diseases, including eosinophilic esophagitis (EoE). This review aims to systematically evaluate current applications of AI, machine learning (ML), and deep learning (DL) methods in EoE characterization and management. Methods: We conducted a systematic review using a registered protocol published in the International Prospective Register of Systematic Reviews (CRD42023451048). The risk of bias and applicability of eligible studies were assessed according to the prediction model study risk of bias assessment tool (PROBAST). We searched PubMed, Embase, and Web of Science to retrieve the articles. The literature review was performed in May 2023. We included original research articles (retrospective or prospective studies) published in English in peer-reviewed journals, studies whose participants were patients with EoE, and studies assessing the application of AI, ML, or DL models. Results: A total of 120 articles were found. After removing 68 duplicates, 52 articles were reviewed based on the title and abstract, and 34 were excluded. Eleven full texts were assessed for eligibility, met the inclusion criteria, and were analyzed for the systematic review. The AI models developed in three studies for identifying EoE based on endoscopic images showed high score performance with an accuracy that ranged from 0.92 to 0.97. Five studies developed AI models that histologically identified EoE with high accuracy (87% to 99%). We also found two studies where the AI model identified subgroups of patients according to their clinical and molecular features. Conclusions: AI technologies could promote more accurate evidence-based management of EoE by integrating the results of molecular signature, clinical, histology, and endoscopic features. However, the era of AI application in medicine is just beginning; therefore, further studies with model validation in the real-world environment are required.

An overview of the applications of AI for detecting anatomical configurations in endodontics.

Artificial Intelligence (AI) technologies, including cognitive computing, machine learning (ML), and deep learning (DL), have the potential to transform healthcare. They enhance patient care, improve symptom differentiation, address medication adherence issues, and facilitate continuous patient support. AI's promise in electronic health records (EHRs) includes extracting valuable insights and improving healthcare infrastructure through advanced disease detection and diagnostics. This study reviews and analyzes AI applications in predictive medicine, focusing on emerging pattern recognition techniques and prediction model algorithms. The goal is to assess the current state of AI applications, identify gaps, and develop models that tackle challenges and ethical dilemmas in predictive medicine. A systematic review was conducted using five major scientific literature databases (ScienceDirect, PubMed, Scopus, IEEE Xplore, and Web of Science). The review covered publications from 2015 to May 2024, employing comprehensive search strings to identify relevant studies. Articles were filtered using inclusion and exclusion criteria, and data were extracted and classified based on AI algorithms, application contexts, and key results. Performance metrics of various AI models and techniques were analyzed following PRISMA guidelines. The review found that deep learning models, particularly Convolutional Neural Networks (CNNs) and Gradient Boosting Machines (GBMs), performed best in personalized medicine. Recurrent Neural Networks (RNNs) and Random Forests (RFs) excelled in disease prediction. Machine learning algorithms, feature selection techniques, data fusion methods, and natural language processing (NLP) techniques improved hospital discharge decision-making. Data fusion methods achieved the highest performance metrics, though challenges such as model interpretability and data complexity were identified.

Artificial Intelligence: The Future of Maxillofacial Prognosis and Diagnosis?

Predictive modeling in reproductive medicine: Where will the future of artificial intelligence research take us?

Background/Objectives: Artificial Intelligence (AI) is improving dentistry through increased accuracy in diagnostics, planning, and workflow automation. AI tools, including machine learning (ML) and deep learning (DL), are being adopted in oral medicine to improve patient care, efficiency, and lessen clinicians’ workloads. AI in dentistry, despite its use, faces an issue of acceptance, with its obstacles including ethical, legal, and technological ones. In this article, a review of current AI use in oral medicine, new technology development, and integration barriers is discussed. Methods: A narrative review of peer-reviewed articles in databases such as PubMed, Scopus, Web of Science, and Google Scholar was conducted. Peer-reviewed articles over the last decade, such as AI application in diagnostic imaging, predictive analysis, real-time documentation, and workflows automation, were examined. Besides, improvements in AI models and critical impediments such as ethical concerns and integration barriers were addressed in the review. Results: AI has exhibited strong performance in radiographic diagnostics, with high accuracy in reading cone-beam computed tomography (CBCT) scan, intraoral photographs, and radiographs. AI-facilitated predictive analysis has enhanced personalized care planning and disease avoidance, and AI-facilitated automation of workflows has maximized administrative workflows and patient record management. U-Net-based segmentation models exhibit sensitivities and specificities of approximately 93.0% and 88.0%, respectively, in identifying periapical lesions on 2D CBCT slices. TensorFlow-based workflow modules, integrated into vendor platforms such as Planmeca Romexis, can reduce the processing time of patient records by a minimum of 30 percent in standard practice. The privacy-preserving federated learning architecture has attained cross-site model consistency exceeding 90% accuracy, enabling collaborative training among diverse dentistry clinics. Explainable AI (XAI) and federated learning have enhanced AI transparency and security with technological advancement, but barriers include concerns regarding data privacy, AI bias, gaps in AI regulating, and training clinicians. Conclusions: AI is revolutionizing dentistry with enhanced diagnostic accuracy, predictive planning, and efficient administration automation. With technology developing AI software even smarter, ethics and legislation have to follow in order to allow responsible AI integration. To make AI in dental care work at its best, future research will have to prioritize AI interpretability, developing uniform protocols, and collaboration between specialties in order to allow AI’s full potential in dentistry.

Chronic rhinosinusitis (CRS) is a prevalent condition frequently evaluated using computed tomography (CT). The application of artificial intelligence (AI) in CRS imaging analysis is expanding; however, comprehensive assessments of its diagnostic performance remain scarce. To systematically review and compare the diagnostic accuracy of AI models for interpreting CT images of CRS, focusing on sensitivity, specificity, accuracy, and area under the curve (AUC). This systematic review followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Six databases were searched for original studies that assessed AI, machine learning, or deep learning models for CRS diagnosis using CT. Eligible studies reported diagnostic performance metrics for human subjects. The extracted data included the AI model type, validation method, imaging modality, reference standard, and diagnostic outcomes. Of the 1502 screened articles, 6 studies involving 2178 patients met the inclusion criteria. Most utilized convolutional neural networks, residual networks (ResNets), or hybrid deep learning models. The sensitivity, specificity, and accuracy ranged from 11.1% to 98.1%, 86.4% to 98.7%, and 63.6% to 98.4%, respectively. AUC values could reach 0.99. AI, particularly ResNets-based models, demonstrates promising diagnostic accuracy for CRS CT interpretation. However, methodological heterogeneity limits comparability, underscoring the need for standardized, multicenter validation and integration of clinical data to enhance generalizability.

The technical execution of root canal treatment procedures demands a thorough understanding and knowledge of root and canal anatomy. Over the decades, and with the aid of various research methods such as staining and clearing, 2D radiographic imaging, sectioning procedures, cone beam computed tomography and micro-computed tomography, many laboratory and clinical studies have been undertaken to understand the root and canal anatomy of the human dentition. This has resulted in a tremendous increase in the body of knowledge with a wide range of qualitative and quantitative presentations of the root and canal anatomy. This review aims to provide a critical analysis for the laboratory and clinical research methods in root and canal anatomy studies. In addition, it aims to identify existing gaps and present insights for directions of future research and ways for translation to clinical endodontics.

Adequate knowledge and accurate characterization of root and canal anatomy is an essential prerequisite for successful root canal treatment and endodontic surgery. Over the years, an ever-increasing body of knowledge related to root and canal anatomy of the human dentition has accumulated. To correct deficiencies in existing systems, a new coding system for classifying root and canal morphology, accessory canals and anomalies has been introduced. In recent years, micro-computed tomography (micro-CT) and cone beam computed tomography (CBCT) have been used extensively to study the details of root and canal anatomy in extracted teeth and within clinical settings. This review aims to discuss the application of the new coding system in studies using micro-CT and CBCT, provide a detailed guide for appropriate characterization of root and canal anatomy and to discuss several controversial issues that may appear as potential limitations for proper characterization of roots and canals.

Obesity is a leading cause of preventable death worldwide. Artificial intelligence (AI), characterized by machine learning (ML) and deep learning (DL), has become an indispensable tool in obesity research. This scoping review aimed to provide researchers and practitioners with an overview of the AI applications to obesity research, familiarize them with popular ML and DL models, and facilitate the adoption of AI applications. We conducted a scoping review in PubMed and Web of Science on the applications of AI to measure, predict, and treat obesity. We summarized and categorized the AI methodologies used in the hope of identifying synergies, patterns, and trends to inform future investigations. We also provided a high-level, beginner-friendly introduction to the core methodologies to facilitate the dissemination and adoption of various AI techniques. We identified 46 studies that used diverse ML and DL models to assess obesity-related outcomes. The studies found AI models helpful in detecting clinically meaningful patterns of obesity or relationships between specific covariates and weight outcomes. The majority (18/22, 82%) of the studies comparing AI models with conventional statistical approaches found that the AI models achieved higher prediction accuracy on test data. Some (5/46, 11%) of the studies comparing the performances of different AI models revealed mixed results, indicating the high contingency of model performance on the data set and task it was applied to. An accelerating trend of adopting state-of-the-art DL models over standard ML models was observed to address challenging computer vision and natural language processing tasks. We concisely introduced the popular ML and DL models and summarized their specific applications in the studies included in the review. This study reviewed AI-related methodologies adopted in the obesity literature, particularly ML and DL models applied to tabular, image, and text data. The review also discussed emerging trends such as multimodal or multitask AI models, synthetic data generation, and human-in-the-loop that may witness increasing applications in obesity research.

Is It Time to Get Rid of Black Boxes and Cultivate Trust in AI?