ReviewBioengineering (Basel, Switzerland)2022
The Role of Machine Learning and Design of Experiments in the Advancement of Biomaterial and Tissue Engineering Research.
Review in Bioengineering (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 31 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
31 citing papers in PubMed.
- Machine learning-guided discovery of a bioactive hydrogel for spatiotemporally orchestrated tendon-to-bone healing.Bioactive materials · 2026Article
- AI for bioactive materials: From material design to biological applications.Bioactive materials · 2026Review
- Machine-Learning-Assisted Quantitative Printability Assessment in Extrusion-Based Bioprinting-A Systems-Engineering Proof-of-Concept.Bioengineering (Basel, Switzerland) · 2026Article
- Article
- Evaluating Alternatives to Fetal Bovine Serum in the Development of Advanced Biomaterial-Based Tumor Models: Overcoming Challenges in Biofabrication.Bioengineering (Basel, Switzerland) · 2026Article
- Dataset on the reproducibility of UHPC mechanical properties under a fixed recipe with controlled production variability.Scientific data · 2026Article
- The Next Phase of 3D Bioprinting: AI-Native Systems-A Narrative Review.Journal of functional biomaterials · 2026Review
- Parameter Optimisation in 3D Extrusion Printing of Polyhydroxybutyrate Using Design of Experiment Methodology.Journal of functional biomaterials · 2026Article
- Implementing QbD for Nano-Pharmaceuticals and Complex Formulations to Achieve Predictable and High-Quality Outcomes.AAPS PharmSciTech · 2026Review
- Bioprocess Engineering for Cultivated Meat.Advances in biochemical engineering/biotechnology · 2026Review
- Indirect 3D printing in tissue engineering: expanding materials used for improved scaffold functionality.Biomedical engineering online · 2025Review
- Rethinking Biomedical Titanium Alloy Design: A Review of Challenges from Biological and Manufacturing Perspectives.Advanced healthcare materials · 2025Review
- Machine learning models for predicting interaction affinity energy between human serum proteins and hemodialysis membrane materials.Scientific reports · 2025Article
- Integrating Modern Technologies into Traditional Anterior Cruciate Ligament Tissue Engineering.Bioengineering (Basel, Switzerland) · 2025Review
- Magnetic Nanocomposite Hydrogels in Orthopedics: Applications and Perspectives.International journal of nanomedicine · 2025Review
- Deep Learning for Predicting Spheroid Viability: Novel Convolutional Neural Network Model for Automating Quality Control for Three-Dimensional Bioprinting.Bioengineering (Basel, Switzerland) · 2025Article
- Artificial Intelligence and/or Machine Learning Algorithms in Microalgae Bioprocesses.Bioengineering (Basel, Switzerland) · 2024Review
- Mobile Application and Machine Learning-Driven Scheme for Intelligent Diabetes Progression Analysis and Management Using Multiple Risk Factors.Bioengineering (Basel, Switzerland) · 2024Article
- Stimuli-Responsive Substrates to Control the Immunomodulatory Potential of Stromal Cells.Biomacromolecules · 2024Review
- Mapping Biomaterial Complexity by Machine Learning.Tissue engineering. Part A · 2024Review
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
Optimisation of tissue engineering (TE) processes requires models that can identify relationships between the parameters to be optimised and predict structural and performance outcomes from both physical and chemical processes. Currently, Design of Experiments (DoE) methods are commonly used for optimisation purposes in addition to playing an important role in statistical quality control and systematic randomisation for experiment planning. DoE is only used for the analysis and optimisation of quantitative data (i.e., number-based, countable or measurable), while it lacks the suitability for imaging and high dimensional data analysis. Machine learning (ML) offers considerable potential for data analysis, providing a greater flexibility in terms of data that can be used for optimisation and predictions. Its application within the fields of biomaterials and TE has recently been explored. This review presents the different types of DoE methodologies and the appropriate methods that have been used in TE applications. Next, ML algorithms that are widely used for optimisation and predictions are introduced and their advantages and disadvantages are presented. The use of different ML algorithms for TE applications is reviewed, with a particular focus on their use in optimising 3D bioprinting processes for tissue-engineered construct fabrication. Finally, the review discusses the future perspectives and presents the possibility of integrating DoE and ML in one system that would provide opportunities for researchers to achieve greater improvements in the TE field.
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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.