ArticleGels (Basel, Switzerland)2024
Leveraging Deep Learning and Generative AI for Predicting Rheological Properties and Material Compositions of 3D Printed Polyacrylamide Hydrogels.
Article in Gels (Basel, Switzerland), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 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
8 citing papers in PubMed.
- Data-Driven Development of Biomedical Hydrogels for Controlled Drug Delivery: Clinical Applications and Emerging Machine-Learning Approaches.Journal of functional biomaterials · 2026Review
- From Processing to Performance: A Multi-Scale Machine Learning Framework for SBS-Modified Asphalt Gels with Multi-Target Correlation and Explainable AI.Gels (Basel, Switzerland) · 2026Article
- Research Progress on Natural Polysaccharide Hydrogels in the Diagnosis and Treatment of Colorectal Cancer.Gels (Basel, Switzerland) · 2026Review
- Artificial neural network as a strategy to predict rheological properties in emulgel formulations.Scientific reports · 2026Article
- Artificial Intelligence Informed Hydrogel Biomaterials in Additive Manufacturing.Gels (Basel, Switzerland) · 2025Review
- Multifunctional Nanomaterial-Integrated Hydrogels for Sustained Drug Delivery: From Synthesis and Characterization to Biomedical Application.Gels (Basel, Switzerland) · 2025Review
- Machine Learning in Gel-Based Additive Manufacturing: From Material Design to Process Optimization.Gels (Basel, Switzerland) · 2025Review
- Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
Abstract
Artificial intelligence (AI) has the ability to predict rheological properties and constituent composition of 3D-printed materials with appropriately trained models. However, these models are not currently available for use. In this work, we trained deep learning (DL) models to (1) predict the rheological properties, such as the storage (G') and loss (G") moduli, of 3D-printed polyacrylamide (PAA) substrates, and (2) predict the composition of materials and associated 3D printing parameters for a desired pair of G' and G". We employed a multilayer perceptron (MLP) and successfully predicted G' and G" from seven gel constituent parameters in a multivariate regression process. We used a grid-search algorithm along with 10-fold cross validation to tune the hyperparameters of the MLP, and found the R
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.