ArticleAdvanced healthcare materials2025
AI-Assisted Design and Evaluation of SLM-Ti64 Implants for Enhanced Bone Regeneration.
Article in Advanced healthcare materials, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
What it found
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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.
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Who cites it
3 citing papers in PubMed.
- Machine Learning and Artificial Intelligence in Metallic Orthopedic Implant Development: A Narrative Review.Materials (Basel, Switzerland) · 2026Review
- Patient-Specific Lattice Implants for Segmental Femoral and Tibial Reconstruction (Part 1): Defect Patterns, Fixation Strategies and Reconstruction Options-A Review.Biomimetics (Basel, Switzerland) · 2026Review
- AI-Assisted Design and Evaluation of SLM-Ti64 Implants for Enhanced Bone Regeneration.Advanced healthcare materials · 2025Article
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
This study presents a comprehensive framework combining Selective Laser Melting (SLM) of Titanium (Ti64) alloys, finite element simulation, and artificial intelligence (AI) to advance orthopedic implants' design and predictive evaluation. Dense Ti64 specimens are fabricated using ten distinct SLM parameter sets to explore the effects of volumetric energy density (VED) on mechanical behavior, porosity distribution, and microstructural integrity. Optimal VED ranges are identified to balance defect minimization and mechanical performance, with porosity levels strongly influencing tensile strength and Young's modulus. These material properties are then integrated into a mechanobiological bone healing model to simulate fracture repair in tibial mid-diaphyseal regions under intramedullary fixation. A mechano-regulation algorithm incorporating deviatoric strain, fluid velocity, and pore pressure guided tissue differentiation and callus evolution over a 4-month healing period. Variations in implant stiffness-achievable via solid and lattice Ti64 configurations demonstrate distinct influences on endochondral ossification, external callus growth, and overall biomechanical recovery. AI-based regression models are trained on simulation outcomes to accelerate evaluation and enable patient-specific predictions to forecast healing trajectories without repeated finite element analyses. This AI-driven framework facilitates real-time prediction of healing outcomes based on implant design, supporting the development of adaptive and optimized orthopedic implants for clinical translation.
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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 OpenQuestion graph.