AI in Endodontics: From Diagnosis to Clinical Decision-Making

— Machine learning and deep learning: key principles and applications in endodontics

— Artificial neural networks and their role in AI-based image analysis

— Image segmentation in endodontics: principles, workflows and clinical applications

— Dice Similarity Coefficient (DSC): evaluating the accuracy of AI-based segmentation

— 3D AI in endodontics: CBCT-based analysis and three-dimensional clinical applications

— Clinical examples of AI-driven 3D segmentation and image analysis.

— AI-assisted detection of periapical lesions and applications in endodontic diagnosis

— AI-based detection of root fractures: 2D and 3D diagnostic approaches

— 2D versus 3D AI diagnosis: capabilities, clinical indications and limitations

— AI advantages and clinical requirements: when and how AI can support clinical decision-making

— Case-Based Reasoning (CBR) for prognosis and prediction of retreatment outcomes

— Robotic surgical endodontics: current technologies, clinical applications and what is already available

— Advantages and limitations of AI and robotic technologies in endodontics

— Analysis of current scientific literature and evidence on AI-assisted endodontic diagnosis and treatment.

— Overview of AI integration in dental radiology

— Early diagnosis supported by AI algorithms

— AI-driven segmentation of anatomical structures and pathologies

— Introduction to radiomics and its clinical applications

— AI applications across different imaging modalities (2D, CBCT, MRI, intraoral scanners)

— Enhancing treatment planning through AI-assisted imaging analysis

— Ethical considerations in AI use within radiology

— Importance of model validation, accuracy, and reliability

— How AI is reshaping clinical decision-making in dental practice.

— Importance of visual communication in modern clinical practice

— Role of visual aids in initial and follow-up examinations

— Overview of diagnostic technologies available to clinicians today

— How patients retain information through visual presentation

— Enhancing patient education with visual and AI-powered tools

— Increasing case acceptance through clearer diagnostic explanations

— Improving diagnostic accuracy and clinician confidence with visual aids

— Identifying key clinical findings to support overall oral health

— How AI enhances diagnostic capabilities and supports decision-making

— Integrating technology and visual tools into daily clinical workflows.