Dental Digital Twins - Advancing Clinical Workflows through Smart Diagnostics

Abstract

The integration of emerging digital technologies into medicine has generated substantial benefits across multiple clinical domains and has driven significant advancements. One of the fields in which the potential offered by Digital Twin technology and Artificial Intelligence opens new methodological and conceptual perspectives is dentistry. This study proposes a patient-centered Digital Twin architecture – digital representation of the oral cavity—designed to support the diagnostic process of dental pathologies based on panoramic radiographs, in correlation with various stages of disease progression. By implementing machine learning techniques, automated classification models are developed to distinguish between unaffected oral cavities, moderate and severe pathological conditions, as well as the presence of prosthetic structures. This approach facilitates the integration of such systems into routine clinical practice and reinforces the paradigm of personalized medicine.

Ähnliche Arbeiten