ArticleAdvanced science (Weinheim, Baden-Wurttemberg, Germany)2025
Machine Learning-Enhanced Optimization for High-Throughput Precision in Cellular Droplet Bioprinting.
Article in Advanced science (Weinheim, Baden-Wurttemberg, Germany), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 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
17 citing papers in PubMed.
- Review
- Harnessing spatiotemporal melatonin delivery from engineered platforms for targeted microenvironment remodeling in peripheral neuropathy.Materials today. Bio · 2026Review
- Soft Robotics and Advanced Technologies for Minimally Invasive Bioprinting: The Future of Internal Organ Repair.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Review
- Advances and applications of organ-on-a-chip technology.Cell reports methods · 2026Review
- Building the next frontier: Artificial intelligence in 3D-printed medicines.Biomaterials translational · 2026Review
- Beyond monolayers: a comparative analysis of 2D cell cultures and 3DFrontiers in bioengineering and biotechnology · 2026Review
- Artificial Intelligence Informed Hydrogel Biomaterials in Additive Manufacturing.Gels (Basel, Switzerland) · 2025Review
- 3D Bioprinting and Artificial Intelligence for Tumor Microenvironment Modeling: A Scoping Review of Models, Methods, and Integration Pathways.Molecular pharmaceutics · 2025Article
- Reconstructing the female reproductive system using 3D bioprinting in tissue engineering.Materials today. Bio · 2025Review
- Building the framework for bioprinted human heart tissue: recent developments and future prospects.Regenerative medicine · 2025Review
- Photocrosslinkable Kidney Decellularized Extracellular Matrix-Based Bioink for 3D Bioprinting.Advanced healthcare materials · 2025Article
- Hydrogel Network Architecture Design Space: Impact on Mechanical and Viscoelastic Properties.Gels (Basel, Switzerland) · 2025Review
- Machine Learning in Gel-Based Additive Manufacturing: From Material Design to Process Optimization.Gels (Basel, Switzerland) · 2025Review
- Machine Learning-Enhanced Optimization for High-Throughput Precision in Cellular Droplet Bioprinting.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Article
- AI-driven 3D bioprinting for regenerative medicine: From bench to bedside.Bioactive materials · 2025Review
- Characterization and Machine Learning-Driven Property Prediction of a Novel Hybrid Hydrogel Bioink Considering Extrusion-Based 3D Bioprinting.Gels (Basel, Switzerland) · 2025Article
- 3D bioprinting of skin equivalents: Towards functional wound healing models.Journal of tissue engineeringReview
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
Organoids produce through traditional manual pipetting methods face challenges such as labor-intensive procedures and batch-to-batch variability in quality. To ensure consistent organoid production, 3D bioprinting platforms offer a more efficient alternative. However, optimizing multiple printing parameters to achieve the desired organoid size remains a time-consuming and costly endeavor. To address these obstacles, machine learning is employed to optimize five critical printing parameters (i.e., bioink viscosity, nozzle size, printing time, printing pressure, and cell concentration), and develop algorithms capable of immediate cellular droplet size prediction. In this study, a high-throughput cellular droplet bioprinter is designed, capable of printing over 50 cellular droplets simultaneously, producing the large dataset required for effective machine learning training. Among the five algorithms evaluated, the multilayer perceptron model demonstrates the highest prediction accuracy, while the decision tree model offers the fastest computation time. Finally, these top-performing machine learning models are integrated into a user-friendly interface to streamline usability. The bioprinting parameter optimization platform develops in this study is expected to create significant synergy when combined with various bioprinting technologies, advancing the scalable production of organoids for a range of applications.
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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.