Journée Scientifique – Conférenciers

Conférenciers

Gordon Lai

Bio of Dr. Gordon Lai

Dr. Gordon Lai received his DDS degree from University of California San Francisco School of Dentistry in 2006 and subsequently completed a one year GPR at the VA Palo Alto.  While serving as the associate dental director at a community clinic in the SF Bay Area for 10 years, he was also active in mentoring residents for the UCSF/NYU Langone AEGD residency program. He subsequently completed his endodontics specialty training at University of Pacific Arthur A. Dugoni School of Dentistry in 2020 and is currently teaching part time as an assistant professor at UOP as well as working in private practice.

One of his passions is teaching students and general dentists worldwide about the fundamentals of endodontics through his Instagram account: predoc_endo_tips. Dr. Lai is also at the forefront of integrating advanced technology into dentistry. His research focuses on incorporating 3D printing, Virtual Reality, and Augmented Reality into both clinical practice and dental education, aiming to revolutionize patient care and learning experiences.

Title: « From Diagnosis to Treatment: Exploring AI’s Impact on Modern Endodontics »

Lecture Abstract:

Artificial intelligence (AI) is revolutionizing various fields, including healthcare, with profound implications for dental practices. This lecture provides a comprehensive overview of AI, examining its fundamental principles, machine learning techniques, and deep learning applications. We’ll explore how AI-driven tools enhance diagnostic accuracy, streamline clinical workflows, and personalize treatment in dentistry, with a focused examination of endodontics. Attendees will gain insights into how AI systems are transforming dental radiography analysis, aiding in pulp vitality assessment, predicting treatment outcomes, and facilitating precision-guided procedures. Through real-world examples, this presentation aims to highlight AI’s potential to improve clinical efficiency, optimize patient outcomes, and pave the way for a more advanced, data-driven approach to endodontic care.

Learning Objectives:

  1. Understand the basic principles and types of artificial intelligence, including machine learning and deep learning.
  2. Recognize the broad applications of AI within healthcare, specifically in diagnostic and procedural aspects of dentistry.
  3. Identify current and emerging applications of AI in endodontics, including AI-assisted diagnostics, treatment planning, and outcome prediction.
  4. Assess the potential benefits and challenges of integrating AI into endodontic practice to improve clinical decision-making and patient care.
  5. Discuss future trends and research directions for AI in endodontics and the ethical considerations of AI adoption in clinical settings.

Raphaël Richert

Bio of Raphaël Richert

Raphaël Richert is currently an Associate Professor and Hospital Practitioner at the Lyon Dental University, Dpt of Restorative Dentistry and Endodontics. His research focuses on artificial intelligence and biomechanics, particularly in patient-specific modelling to enhance personalized dental care. Prior to this role, he maintained a private practice limited to endodontics.

Summary

The integration of artificial intelligence (AI) and digital technologies has significantly transformed dental practice, notably through CAD/CAM systems, and is now set to revolutionize the field of endodontics. Central to this transformation is the concept of digital twins—virtual replicas of patients’ dental anatomy that are developed through advanced imaging and simulation technologies. These digital models enable endodontists to personalize plan treatments by learning and predicting key anatomical and biological features.

Before treatment, AI-driven models promise to enhance clinical decision-making by analyzing clinical signs, tooth morphology and simulating biomechanical behavior in various scenarios. These tools help practitioners forecast challenges such as canal anatomy variations, root fractures, or the response of dental structures to treatments. During treatment, AI applications, including augmented reality for guided endodontics and robot-assisted interventions, aim to support practitioners in limiting complications like perforations or instrument fractures. During treatment, AI applications, including augmented reality for guided endodontics and robot-assisted interventions, aim to support practitioners in minimizing complications like perforations or instrument fractures. Despite its potential, it is however important to remember that practitioners and industry stakeholders share the responsibility of safely implementing AI, particularly within the framework of the current EU AI Act. Within these constraints, implementing AI in endodontics still faces multiple challenges, particularly ensuring transparency, explainability, and generalizability to populations different from the trained dataset.

Aims and Objectives:

This afternoon will provide participants with an evidenced-based update on the hot topic of AI in endodontics. The key aims are:

  • Gain an understanding of how digital twins and AI technologies can enhance diagnosis, treatment planning, and predictive outcomes in endodontics.
  • Acquire knowledge of the ethical and regulatory considerations associated with AI in endodontics, ensuring transparent and explainable use in practice.

Plan

  1. The Role of Digital Twins in Daily Dental Practice (Collet et al. 2024)(Richert et al. 2017)(Lahoud et al. 2022)
  2. AI Applications for Prediction: Shape Analysis and Biomechanical Simulation (Richert et al. 2021; Lahoud et al. 2023; Binvignat et al. 2024; Lahoud et al. 2024) (Walter et al. 2025 submitted)
  • Innovations in Endodontics: Guided Techniques, Augmented Reality, and Robot-Assisted Interventions (Bittar et al. 2023) (Ducret et al. 2025 submitted)
  1. Challenges and Limitations of AI : Benchmarking and Legal Framework (Mörch et al. 2021; Ducret et al. 2022; Ducret et al. 2024)

Reference

Binvignat P, Chaurasia A, Lahoud P, Jacobs R, Pokhojaev A, Sarig R, Ducret M, Richert R. 2024. Isotopological remeshing and statistical shape analysis: Enhancing premolar tooth wear classification and simulation with machine learning. J Dent. 149(April):105280. https://doi.org/10.1016/j.jdent.2024.105280.

Bittar E, Binvignat P, Villat C, Maurin J, Ducret M, Richert R. 2023. Assessment of guide fitting using an intra-oral scanner: An in vitro study. J Dent. 135(2):104590. https://doi.org/10.1016/j.jdent.2023.104590.

Collet P, Tra R, Reitmann A, Valette S, Hoyek N. 2024. Spatial Abilities and Endodontic Access Cavity Preparation : Implications for Dental Education. Eur J Dent Educ.:1–8.

Ducret M, Mörch C-M, Karteva T, Fisher J, Schwendicke F. 2022. Artificial intelligence for sustainable oral healthcare. J Dent. 127:104344.

Ducret M, Wahal E, Gruson D, Amrani S, Richert R, Schwendicke F. 2024. Trustworthy Artificial Intelligence in Dentistry : Learnings from the EU AI Act. J Dent Res.:1–6.

Lahoud P, Badrou A, Ducret M, Farges J, Jacobs R, Bel-brunon A, Ezeldeen M, Blal N, Richert R. 2023. Real-time simulation of the transplanted tooth using model order reduction. Front Bioeng Biotechnol.(June):1–6.

Lahoud P, Jacobs R, Boisse P, EzEldeen M, Ducret M, Richert R. 2022. Precision medicine using patient-specific modelling: state of the art and perspectives in dental practice. Clin Oral Investig. 26(8):5117–5128. https://doi.org/10.1007/s00784-022-04572-0.

Lahoud P, Jacobs R, Elahi SA, Ducret M, Lauwers W, van Lenthe GH, Richert R, EzEldeen M. 2024. Developing Advanced Patient-Specific In-Silico Models: A New Era in Biomechanical Analysis of Tooth Autotransplantation. J Endod.:108773. https://linkinghub.elsevier.com/retrieve/pii/S0099239924001572.

Mörch CM, Atsu S, Cai W, Li X, Madathil SA, Liu X, Mai V, Tamimi F, Dilhac MA, Ducret M. 2021. Artificial Intelligence and Ethics in Dentistry: A Scoping Review. J Dent Res. 100(13):1452–1460. https://doi.org/10.1177/00220345211013808.

Richert R, Farges JC, Villat C, Valette S, Boisse P, Ducret M. 2021. Decision Support for Removing Fractured Endodontic Instruments: A Patient-Specific Approach. Appl Sci. 11(6):2602.

Richert R, Goujat A, Venet L, Viguie G, Viennot S, Robinson P, Farges JC, Fages M, Ducret M. 2017. Intraoral Scanner Technologies: A Review to Make a Successful Impression. J Healthc Eng. 2017.