ScaffoldForge: Bioinspired generative design of 3D-printed bone scaffolds via conditional diffusion for large segmental bone defect reconstruction

Yaohua Zhang, Hongyue Zhang and Huaqing Yang

PLOS ONE, 2026, vol. 21, issue 10, 1-29

Abstract: We present ScaffoldForge, a 3D conditional diffusion framework for the generative design of bone scaffolds. ScaffoldForge maps a set of standardized trabecular descriptors (bone volume fraction BV/TV, trabecular thickness Tb.Th, trabecular spacing Tb.Sp, trabecular number Tb.N, connectivity density Conn.D, structure model index SMI, degree of anisotropy DA, and permeability k) to a 3D scaffold architecture in a single forward pass. A multi-objective conditioning mechanism couples these descriptors through adaptive instance normalization, and a physics-constrained loss adds empirical penalties for stiffness, manufacturing feasibility, and perfusion, evaluated on the denoiser’s reconstruction of the clean scaffold so that the penalties carry gradient. To address the scarcity of paired geometry–property data, we describe a procedural pipeline whose generated scaffolds reproduce the morphometric distributions of real micro-CT references. In an in silico evaluation on three benchmark datasets, ScaffoldForge attains an estimated 56.6 MPa compressive strength (8.2% over topology optimization; paired t-test, p = 0.043, n = 5 seeds) and reduces design-time cost from 8,400–12 seconds per design, a 700× speedup. Generated structures are diverse rather than memorized, and morphometric accuracy holds across all seven shape descriptors. Three analyses probe the reliability of these results given the small training corpus. Equivalence testing replaces the earlier argument from non-significant difference tests: the procedural and real descriptor distributions are equivalent at a medium margin of 0.5 standard deviations for all seven descriptors, but the 50 available real reference volumes cannot establish equivalence at a strict 0.3-standard-deviation margin for five of them. A classifier two-sample test shows that generated scaffolds retain a measurable but shrinking signature of the procedural generator. Enlarging the corpus from 250 to 2,050 scaffolds leaves in-distribution accuracy nearly unchanged while reducing the cross-dataset descriptor error on unseen anatomy from 0.079 to 0.058, which localizes the cost of the small corpus in generalization rather than in fit. We further illustrate a CT-to-scaffold pipeline for personalized generation. All results are computational; physical, biological, manufacturing, and regulatory validation remain future work.

Date: 2026
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Persistent link: https://EconPapers.repec.org/RePEc:plo:pone00:0361041

DOI: 10.1371/journal.pone.0361041

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