ReviewFrontiers in bioengineering and biotechnology2019
Engineering Tissue Fabrication With Machine Intelligence: Generating a Blueprint for Regeneration.
Review in Frontiers in bioengineering and biotechnology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 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
13 citing papers in PubMed.
- Engineered Tissue Models to Decode Host-Microbiota Interactions.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Review
- Computational methods for biofabrication in tissue engineering and regenerative medicine - a literature review.Computational and structural biotechnology journal · 2024Review
- Harnessing the power of machine learning into tissue engineering: current progress and future prospects.Burns & trauma · 2024Review
- The main tasks of a semiotics of artificial intelligence.Language and semiotic studies · 2023Article
- Machine Learning in Tissue Engineering.Tissue engineering. Part A · 2023Review
- State-of-the-art techniques for imaging the vascular microenvironment in craniofacial bone tissue engineering applications.American journal of physiology. Cell physiology · 2022Review
- The Role of Machine Learning and Design of Experiments in the Advancement of Biomaterial and Tissue Engineering Research.Bioengineering (Basel, Switzerland) · 2022Review
- Machine Learning Reveals a General Understanding of Printability in Formulations Based on Rheology Additives.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2022Article
- Vascularization in Bioartificial Parenchymal Tissue: Bioink and Bioprinting Strategies.International journal of molecular sciences · 2022Review
- Digitally Fabricated and Naturally Augmented In Vitro Tissues.Advanced healthcare materials · 2021Review
- Towards the Experimentally-InformedFrontiers in bioengineering and biotechnology · 2021Article
- Article
- Supervised machine learning for automated classification of human Wharton's Jelly cells and mechanosensory hair cells.PloS one · 2021Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
Abstract
Regenerating lost or damaged tissue is the primary goal of Tissue Engineering. 3D bioprinting technologies have been widely applied in many research areas of tissue regeneration and disease modeling with unprecedented spatial resolution and tissue-like complexity. However, the extraction of tissue architecture and the generation of high-resolution blueprints are challenging tasks for tissue regeneration. Traditionally, such spatial information is obtained from a collection of microscopic images and then combined together to visualize regions of interest. To fabricate such engineered tissues, rendered microscopic images are transformed to code to inform a 3D bioprinting process. If this process is augmented with data-driven approaches and streamlined with machine intelligence, identification of an optimal blueprint can become an achievable task for functional tissue regeneration. In this review, our perspective is guided by an emerging paradigm to generate a blueprint for regeneration with machine intelligence. First, we reviewed recent articles with respect to our perspective for machine intelligence-driven information retrieval and fabrication. After briefly introducing recent trends in information retrieval methods from publicly available data, our discussion is focused on recent works that use machine intelligence to discover tissue architectures from imaging and spectral data. Then, our focus is on utilizing optimization approaches to increase print fidelity and enhance biomimicry with machine learning (ML) strategies to acquire a blueprint ready for 3D bioprinting.
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.