ArticleMaterials (Basel, Switzerland)2021
Artificial Intelligence Aided Design of Tissue Engineering Scaffolds Employing Virtual Tomography and 3D Convolutional Neural Networks.
Article in Materials (Basel, Switzerland), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 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
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Who cites it
24 citing papers in PubMed.
- Review
- Artificial Intelligence and Digital Workflow in Craniofacial Bone Tissue Engineering: From Cone-Beam Computed Tomography (CBCT) to Personalized Bioceramic Implants.Dentistry journal · 2026Review
- Artificial intelligence for design strategies of tissue engineering materials.Fundamental research · 2026Review
- Bioprinting Organs-Science or Fiction?-A Review From Students to Students.Advanced healthcare materials · 2026Review
- Bioengineered Teeth and Regenerative Strategies: Advances in Tissue Engineering and Stem Cell Applications.Annals of biomedical engineering · 2026Review
- Biomimetic electrospun scaffolds for engineered heart tissue: from design parameters to drug testing platforms.Frontiers in bioengineering and biotechnology · 2026Review
- Sustainable hemocompatibility prediction of electrospun nanomaterials using SEM-driven CNN ensemble learning aligned with ISO 10993.PloS one · 2026Article
- Indirect 3D printing in tissue engineering: expanding materials used for improved scaffold functionality.Biomedical engineering online · 2025Review
- Cell condensation initiates organogenesis: the role of actin dynamics in supracellular self-organizing process.Cell & bioscience · 2025Review
- Multimodal AI in Biomedicine: Pioneering the Future of Biomaterials, Diagnostics, and Personalized Healthcare.Nanomaterials (Basel, Switzerland) · 2025Review
- Advancements in 3D printing technologies for personalized treatment of osteonecrosis of the femoral head.Materials today. Bio · 2025Review
- Integrating Modern Technologies into Traditional Anterior Cruciate Ligament Tissue Engineering.Bioengineering (Basel, Switzerland) · 2025Review
- Blueprints of Architected Materials: A Guide to Metamaterial Design for Tissue Engineering.Advanced materials (Deerfield Beach, Fla.) · 2024Review
- Toward Fully Automated Personalized Orthopedic Treatments: Innovations and Interdisciplinary Gaps.Bioengineering (Basel, Switzerland) · 2024Review
- AI-Based Metamaterial Design.ACS applied materials & interfaces · 2024Review
- Applications of artificial intelligence in regenerative dentistry: promoting stem cell therapy and the scaffold development.Frontiers in cell and developmental biology · 2024Review
- Harnessing the power of machine learning into tissue engineering: current progress and future prospects.Burns & trauma · 2024Review
- Review
- Bone Regeneration Induced by Patient-Adapted Mg Alloy-Based Scaffolds for Bone Defects: Present and Future Perspectives.Biomimetics (Basel, Switzerland) · 2023Review
- Development of Bioactive Scaffolds for Orthopedic Applications by Designing Additively Manufactured Titanium Porous Structures: A Critical Review.Biomimetics (Basel, Switzerland) · 2023Review
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Design requirements for different mechanical metamaterials, porous constructions and lattice structures, employed as tissue engineering scaffolds, lead to multi-objective optimizations, due to the complex mechanical features of the biological tissues and structures they should mimic. In some cases, the use of conventional design and simulation methods for designing such tissue engineering scaffolds cannot be applied because of geometrical complexity, manufacturing defects or large aspect ratios leading to numerical mismatches. Artificial intelligence (AI) in general, and machine learning (ML) methods in particular, are already finding applications in tissue engineering and they can prove transformative resources for supporting designers in the field of regenerative medicine. In this study, the use of 3D convolutional neural networks (3D CNNs), trained using digital tomographies obtained from the CAD models, is validated as a powerful resource for predicting the mechanical properties of innovative scaffolds. The presented AI-aided or ML-aided design strategy is believed as an innovative approach in area of tissue engineering scaffolds, and of mechanical metamaterials in general. This strategy may lead to several applications beyond the tissue engineering field, as we analyze in the discussion and future proposals sections of the research study.
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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.