Gemelos digitales en endodoncia: de reactivos a predictivos - una nueva frontera hacia el tratamiento de conductos...

Objectives To describe the potential of digital twin (DT) technology to enhance personalised root canal treatment within endodontics, drawing upon its established applications in various healthcare sectors.Discussion DT models are gaining traction as transformative tools for enabling individualised decision-making across different medical disciplines. These models leverage multimodal patient data to simulate physiological and clinical outcomes. In endodontics, DTs could facilitate the simulation of intricate parameters, such as root canal morphology, access cavity preparation, microbial biofilm dynamics, disinfection protocols, clinical techniques for root canal preparation and filling, and the long-term behaviour of restorations. The application of DTs will empower clinicians to formulate more tailored treatment plans and improve prognostic predictions. Beyond their clinical applications, DTs can enrich research settings, linking laboratory research with tailored patient care. While deploying DTs in endodontics remains largely aspirational at this stage, it has the potential to shift the paradigm from standardised approaches to personalised treatments. Key challenges to address include data standardisation, interoperability among systems, ethical regulations, and the need for specialised clinician training. This article suggests actionable strategies for the translational development of DTs in endodontics, inspired by successful frameworks in other medical domains.Conclusion DT models can reshape the vision in endodontics, facilitating real-time, patient-specific simulation and clinical decision-making. Moreover, DT technology presents a cohesive framework that could enhance precision in endodontic practice while also expediting the translation of research findings into clinical applications. This advancement may lead to the development of personalised and predictive approaches to root canal treatment, significantly improving patient outcomes.