ReviewMicromachines2022
Optimized 3D Bioprinting Technology Based on Machine Learning: A Review of Recent Trends and Advances.
Review in Micromachines, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 36 papers.
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
36 citing papers in PubMed.
- Next-Generation Bioinks in 3D Bioprinting: Advances, Challenges, and Emerging Opportunities.ACS omega · 2026Review
- Digital light processing bioprinting: bioink innovations and applications in tissue and organ regeneration.Journal of biological engineering · 2026Review
- Emerging Nano Bioinks in Bioprinting: Functional Materials, Engineering Strategies, and Biomedical Applications.Materials (Basel, Switzerland) · 2026Review
- AI-Guided 4D Printing of Carnivorous Plants-Inspired Microneedles for Accelerated Wound Healing.Advanced materials (Deerfield Beach, Fla.) · 2026Article
- Biomaterial Strategies for Three-Dimensional Bioprinting and Drug Delivery Application.Materials (Basel, Switzerland) · 2026Review
- In Situ Characterisation of Hydrogels via Dynamic Interface Printing.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Modeling of Biomechanical and Functional Parameters of Hydrogel-Cell Composites Fabricated by 3D Bioprinting Using AI-Supported Approach.Materials (Basel, Switzerland) · 2026Article
- Anionic Polysaccharides: Promising 3D Bioink Candidates for Tissue Engineering.Bioengineering (Basel, Switzerland) · 2026Review
- Bio-Printed PCL Tracheal Graft in a Large Animal Model: Reproducible Short-Segment Regeneration and Preliminary Upgraded Long-Segment Reconstruction.Bioengineering (Basel, Switzerland) · 2026Article
- A Formal Optimization-Oriented Design Framework for Predictive Extrusion-Based 3D Bioprinting.Biomimetics (Basel, Switzerland) · 2026Article
- Quantitative Assessment of Hydrogel Printability in Extrusion Bioprinting.Gels (Basel, Switzerland) · 2026Review
- Mechanobiology-based strategies for the maturation of biofabricated cartilage constructs.Frontiers in bioengineering and biotechnology · 2026Review
- The Synergy of Artificial Intelligence and 3D Bioprinting: Unlocking New Frontiers in Precision and Tissue Fabrication.Advanced functional materials · 2025Article
- Advancing Bioprinting Technology Utilizing Portable Bioprinters: From Various Device Designs to Dental Applications.Annals of biomedical engineering · 2025Review
- Innovative animal models for surgical interventions and implant biocompatibility: a translational perspective.Annals of medicine and surgery (2012) · 2025Review
- Recent Advances in 3D Bioprinting of Porous Scaffolds for Tissue Engineering: A Narrative and Critical Review.Journal of functional biomaterials · 2025Review
- Enhancing 3D Printing of Gelatin/Siloxane-Based Cellular Scaffolds Using a Computational Model.Polymers · 2025Article
- Review
- Multimodal AI in Biomedicine: Pioneering the Future of Biomaterials, Diagnostics, and Personalized Healthcare.Nanomaterials (Basel, Switzerland) · 2025Review
- Branding a New Technological Outlook for Future Orthopaedics.Bioengineering (Basel, Switzerland) · 2025Review
Corrections and comments
- Erratum issued
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The need for organ transplants has risen, but the number of available organ donations for transplants has stagnated worldwide. Regenerative medicine has been developed to make natural organs or tissue-like structures with biocompatible materials and solve the donor shortage problem. Using biomaterials and embedded cells, a bioprinter enables the fabrication of complex and functional three-dimensional (3D) structures of the organs or tissues for regenerative medicine. Moreover, conventional surgical 3D models are made of rigid plastic or rubbers, preventing surgeons from interacting with real organ or tissue-like models. Thus, finding suitable biomaterials and printing methods will accelerate the printing of sophisticated organ structures and the development of realistic models to refine surgical techniques and tools before the surgery. In addition, printing parameters (e.g., printing speed, dispensing pressure, and nozzle diameter) considered in the bioprinting process should be optimized. Therefore, machine learning (ML) technology can be a powerful tool to optimize the numerous bioprinting parameters. Overall, this review paper is focused on various ideas on the ML applications of 3D printing and bioprinting to optimize parameters and procedures.
Indexed as
Identifiers
What OpenQuestion 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 OpenQuestion graph.