ArticleBiofabrication2021
Compensating the cell-induced light scattering effect in light-based bioprinting using deep learning.
Article in Biofabrication, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
23 citing papers in PubMed.
- 3D bioprinting of tissues and organs for systemic diseases and localized injuries.Military Medical Research · 2026Review
- Spatially Resolved Photostimulation of Cardiac Activity Using Optoelectronic Peptides.ACS central science · 2026Article
- dECM bioinks for 3D bioprinted tumor models: Advances, challenges, and drug screening.iScience · 2026Review
- Light-assisted 3D bioprinting of tough hydrogels in biomedical applications.Materials today. Bio · 2026Review
- Digital light processing bioprinting: bioink innovations and applications in tissue and organ regeneration.Journal of biological engineering · 2026Review
- Gravity-Tolerant In-Flight 3D Bioprinting Enabled by Stereolithography for Space Tissue Engineering.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Unconventional bioprinting modalities for advanced tissue biofabrication.Biomaterials · 2026Review
- Article
- Advanced cell-adaptable hydrogels for bioprinting.Bioactive materials · 2025Review
- The Synergy of Artificial Intelligence and 3D Bioprinting: Unlocking New Frontiers in Precision and Tissue Fabrication.Advanced functional materials · 2025Article
- 3D Bioprinting and Artificial Intelligence for Tumor Microenvironment Modeling: A Scoping Review of Models, Methods, and Integration Pathways.Molecular pharmaceutics · 2025Article
- Adaptive and context-aware volumetric printing.Nature · 2025Article
- Iohexol as a refractive index tuning agent for bioinks in high cell density bioprinting.Biomaterials science · 2025Article
- AI-driven 3D bioprinting for regenerative medicine: From bench to bedside.Bioactive materials · 2025Review
- Lithography-based 3D printing of hydrogels.Nature reviews bioengineering · 2025Article
- Bioprinting vascularized skin analogs: a stepwise approach.Burns & trauma · 2025Review
- Leveraging printability and biocompatibility in materials for printing implantable vessel scaffolds.Materials today. Bio · 2024Review
- Photoinhibiting via simultaneous photoabsorption and free-radical reaction for high-fidelity light-based bioprinting.Nature communications · 2023Article
- High cell density and high-resolution 3D bioprinting for fabricating vascularized tissues.Science advances · 2023Article
- Light-based vat-polymerization bioprinting.Nature reviews. Methods primers · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors.
Funding
Abstract
Digital light processing (DLP)-based three-dimensional (3D) printing technology has the advantages of speed and precision comparing with other 3D printing technologies like extrusion-based 3D printing. Therefore, it is a promising biomaterial fabrication technique for tissue engineering and regenerative medicine. When printing cell-laden biomaterials, one challenge of DLP-based bioprinting is the light scattering effect of the cells in the bioink, and therefore induce unpredictable effects on the photopolymerization process. In consequence, the DLP-based bioprinting requires extra trial-and-error efforts for parameters optimization for each specific printable structure to compensate the scattering effects induced by cells, which is often difficult and time-consuming for a machine operator. Such trial-and-error style optimization for each different structure is also very wasteful for those expensive biomaterials and cell lines. Here, we use machine learning to learn from a few trial sample printings and automatically provide printer the optimal parameters to compensate the cell-induced scattering effects. We employ a deep learning method with a learning-based data augmentation which only requires a small amount of training data. After learning from the data, the algorithm can automatically generate the printer parameters to compensate the scattering effects. Our method shows strong improvement in the intra-layer printing resolution for bioprinting, which can be further extended to solve the light scattering problems in multilayer 3D bioprinting processes.
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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.