AI in Endodontics: From Diagnosis to Clinical Decision-Making
AI in Endodontics: From Diagnosis to Clinical Decision-Making
Course overview
Artificial intelligence is rapidly changing the way endodontic conditions are detected, analysed and managed. From automated image segmentation and CBCT analysis to the detection of periapical lesions and root fractures, AI is creating new possibilities for more efficient and data-driven clinical decision-making.
This course focuses on the principles and clinical applications of AI in endodontics, combining the technological foundations of machine learning and deep learning with practical applications in 2D and 3D image analysis, diagnosis, prognosis and treatment planning.
Participants will explore how AI systems work, how their accuracy can be evaluated, and where these technologies can add value in everyday endodontic practice.
During the course, you will explore:
The fundamentals of machine learning, deep learning and artificial neural networks in endodontics
AI-based image segmentation and 3D analysis, including CBCT applications and the Dice Similarity Coefficient (DSC)
AI-assisted detection of periapical lesions and root fractures using 2D and 3D imaging
Case-Based Reasoning (CBR) for prognosis and prediction of retreatment outcomes
Emerging applications of AI and robotic surgical endodontics, including current technologies, clinical potential and limitations
Current scientific evidence on AI in endodontics through analysis of the literature and clinical cases.
Course lessons
Lesson 1. AI Foundations and 3D Segmentation in Endodontics
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.
Lesson 2. AI in Endodontic Diagnosis, Prognosis and Treatment
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.
Lesson 3. AI-Powered Radiological Diagnostics in Dentistry: From Panoramic Imaging to CBCT and Beyond
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.
Lesson 4. Transformative Diagnostics: Using Visual Tools and AI to Enhance Diagnosis and Case Acceptance
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.
