A machine learning-based multiscale model to predict bone formation in scaffolds.

Area of Science:

• Biomaterials Science
• Computational Biology
• Regenerative Medicine

Background:

• Scaffold tissue engineering aims to regenerate bone tissue using computational modeling and non-invasive imaging.
• Existing numerical methods for multiscale bone ingrowth modeling are computationally intensive.
• There is a need for efficient in silico methods to complement in vivo studies.

Purpose of the Study:

• To develop and validate a machine learning (ML)-based approach for predicting bone ingrowth in bulk tissue scaffolds.
• To assess the accuracy and efficiency of the ML model against conventional finite element (FE 2 ) models.
• To provide a subject-specific computational tool for predicting bone tissue regeneration.

Main Methods:

• Developed a machine learning (ML) model correlating computational predictions with a 12-month longitudinal animal study.
• Utilized non-invasive imaging and scaffold treatment data from sheep tibia defects.
• Compared ML-based time-dependent bone ingrowth predictions with multilevel finite element (FE 2 ) modeling.

Main Results:

• The ML-based approach demonstrated satisfactory accuracy in predicting bone ingrowth outcomes.
• The computational efficiency of the ML model surpassed conventional FE 2 methods.
• The study established a correlation between in silico predictions and in vivo bone regeneration.

Conclusions:

• Machine learning offers an efficient and accurate method for predicting bone ingrowth in tissue engineering scaffolds.
• The developed ML model provides a viable complement to in vivo studies for evaluating scaffold treatments.
• This approach facilitates subject-specific prediction of bone tissue regeneration in personalized scaffolding systems.