Biomechanics in Dental Implants: Analysis Using Artificial Intelligence Systems

doi:10.4067/s0718-381x2025000300224

Q4 · Health ProfessionsImpact Factor · 0.197

AutoresAritza Brizuela‐Velasco, Saray Férnandez-Hernández, José Gaviria-de la Puerta, Francisco T Barbosa, Igone Porto Gómez, Iker Bellanco-de la Pinta, Esteban Pérez-Pevida, Daniel Robles

Por qué importa

Este análisis bibliométrico mediante inteligencia artificial examina la investigación en biomecánica de implantes dentales. Se han identificado las variables dependientes e independientes más estudiadas, así como los procedimientos de ensayo más comunes, destacando el tipo de carga, la transferencia de carga y el torque de inserción como focos principales. La prevalencia de estudios in vitro subraya la necesidad de mayor investigación in vivo para correlacionar los resultados de laboratorio con la práctica clínica. Se señalan áreas clave como la calidad ósea y el tipo de conexión del implante como aspectos insuficientemente explorados, a pesar de su relevancia clínica. El estudio proporciona una visión de las lagunas de conocimiento y sugiere una dirección para futuras investigaciones, facilitando la mejora de los resultados clínicos en implantología.

Abstract original

This study aims to analyze the field of dental implant biomechanics using a novel AI-powered bibliometric approach.By leveraging machine learning and natural language processing, we mapped key research trends, identified dependent and independent variables, and highlighted commonly used testing procedures.This analysis seeks to pinpoint underexplored areas in biomechanics research and offer a roadmap for future studies.We conducted a systematic review of 1,512 full-text articles from the PubMed database.Using the Rapid Automatic Keyword Extraction (RAKE) algorithm, key concepts were identified and classified into three categories: dependent variables, independent variables, and procedures.Advanced co-occurrence analysis was then applied to visualize the interrelations between these terms and their prevalence across the body of literature.Our analysis revealed that the most frequently studied independent variable is loading type, while the most prominent dependent variable is loading transfer, and the most employed procedure is insertion torque measurement.The study also uncovered a reliance on in vitro methodologies, indicating the need for more in vivo research to bridge the gap between laboratory findings and clinical practice.Several important biomechanical factors, such as bone quality and implant connection type, remain underexplored despite their potential clinical impact.Our findings reveal critical knowledge gaps in the field of dental implant biomechanics and underscore the importance of in vivo research to improve clinical outcomes.By combining bibliometric analysis with AI-driven keyword extraction, this study introduces a reproducible, scalable approach to mapping research landscapes in dental science.

Leer texto completo ↗DOI · 10.4067/s0718-381x2025000300224

Autores Doctor&Cols

Cómo citar

Brizuela‐Velasco A, Férnandez-Hernández S, Puerta JGL, Barbosa FT, Gómez IP, Pinta IBL, et al. Biomechanics in Dental Implants: Analysis Using Artificial Intelligence Systems. International journal of odontostomatology. 2025. doi:10.4067/s0718-381x2025000300224