ReviewCureus2026
Exploring the Intricacies of Finite Element Modeling of 3D-Printed Scaffolds for Musculoskeletal Applications: An In-Depth Review.
Review in Cureus, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
1 citing paper in PubMed.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Finite element analysis (FEA) is redefining how three-dimensional (3D)-printed bone scaffolds are designed and validated. By digitally predicting stress, strain, and deformation before fabrication, FEA is transforming the field of 3D-printed bone scaffolds by offering a predictive framework to design and validate mechanically robust, biologically active constructs. This review summarizes how FEA-driven strategies optimize scaffold geometry, pore architecture, and material properties, ranging from polymer-ceramic composites to hydrogel blends, under physiological loads. We highlight multiscale modeling approaches that connect microscale porosity to overall strength and discuss live integration of printer feedback for rapid design iterations. Experimental and early clinical validations reveal FEA predictions within single-digit error margins and demonstrate scaffold-guided bone ingrowth in patient-specific implants. Finally, we examine emerging AI-enhanced methodologies for real-time optimization, challenges in modeling degradation and cell remodeling, and propose standardized workflows to accelerate the clinical translation of FEA-informed bioprinted bone scaffolds.
Indexed as
Identifiers
What Socratic holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.