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Updated: Jul 19, 2025

Modeling Primary Bone Tumors and Bone Metastasis with Solid Tumor Graft Implantation into Bone

Modeling Primary Bone Tumors and Bone Metastasis with Solid Tumor Graft Implantation into Bone

Published on: September 9, 2020

Modeling new trends in bone regeneration, using the BERTopic approach.

Stefano Guizzardi¹, Maria Teresa Colangelo¹, Prisco Mirandola¹

¹Department of Medicine & Surgery, Histology & Embryology Lab, University of Parma, Parma, 43126, Italy.

Regenerative Medicine

|August 14, 2023

Summary

Artificial intelligence (AI) tools like BERTopic can automatically screen bone regeneration literature, identifying 372 research topics. This AI approach efficiently tracks progress in emerging areas like 3D printing and extracellular vesicles.

Keywords:

artificial intelligence bone regeneration deep learning natural language processing tissue engineering trending topics

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Last Updated: Jul 19, 2025

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Area of Science:

Biomedical Engineering
Regenerative Medicine
Materials Science

Background:

Bibliometric surveys are essential for tracking research progress but are time-consuming and difficult to scale.
The field of bone regeneration is rapidly expanding, necessitating more efficient methods for literature analysis.
Artificial intelligence offers potential solutions for automating the screening of vast scientific literature.

Purpose of the Study:

To evaluate the utility of artificial intelligence, specifically the BERTopic algorithm, for automatically identifying research topics in bone regeneration.
To assess the scalability and efficiency of AI-driven bibliometric analysis compared to traditional methods.
To map research similarities and identify emerging hotspots within the bone regeneration field.

Main Methods:

A corpus of MEDLINE manuscripts related to bone regeneration was analyzed using the BERTopic algorithm.
BERTopic was employed to detect latent topics, map their interrelationships, and visualize research trends.
The algorithm was used to automatically screen and categorize a large volume of scientific literature.

Main Results:

The BERTopic algorithm successfully identified 372 distinct topics within the bone regeneration literature.
Analysis revealed the increasing significance of novel research areas, including 3D printing and extracellular vesicles.
The study demonstrated the capability of AI to highlight current and emerging research hotspots.

Conclusions:

The BERTopic algorithm is a suitable tool for establishing automated screening routines in scientific research.
AI-powered bibliometric analysis can efficiently track the progress and identify key trends in rapidly evolving fields like bone regeneration.
This approach facilitates a more dynamic and scalable understanding of scientific advancements.