ArticleBiosensors2022
Machine Learning-Enabled Prediction of 3D-Printed Microneedle Features.
Article in Biosensors, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 30 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
30 citing papers in PubMed.
- Barrier-disrupting microneedle technology: Overcoming physical, physiological, and pathological skin defenses to enhance therapeutic efficacy.Bioactive materials · 2026Review
- Applications of 3D Printing and Artificial Intelligence in Healthcare Management: A Narrative Review.Bioengineering (Basel, Switzerland) · 2026Review
- Application and validation of AI-assisted 3D-Printed gastroduodenal anatomical variation models in specialized nursing training.Frontiers in bioengineering and biotechnology · 2026Article
- Smart micro/nanoneedles for gene delivery.Iranian journal of basic medical sciences · 2026Review
- Article
- Review
- The Future of Alopecia Treatment: Plant Extracts, Nanocarriers, and 3D Bioprinting in Focus.Pharmaceutics · 2025Review
- Use of Computational Intelligence in Customizing Drug Release from 3D-Printed Products: A Comprehensive Review.Pharmaceutics · 2025Review
- Advancements in vaccine delivery: harnessing 3D printing for microneedle patch technology.Annals of medicine and surgery (2012) · 2025Review
- Designing Multifunctional Microneedles in Biomedical Engineering: Materials, Methods, and Applications.International journal of nanomedicine · 2025Review
- Optimizing Solid Microneedle Design: A Comprehensive ML-Augmented DOE Approach.ACS measurement science au · 2024Article
- Recent advances and applications of artificial intelligence in 3D bioprinting.Biophysics reviews · 2024Review
- Machine Learning Assists in the Design and Application of Microneedles.Biomimetics (Basel, Switzerland) · 2024Review
- AI-Based Metamaterial Design.ACS applied materials & interfaces · 2024Review
- Let's Print an Ecology in 3D (and 4D).Materials (Basel, Switzerland) · 2024Review
- Deep learning-augmented T-junction droplet generation.iScience · 2024Article
- 3D Printing of Dietary Products for the Management of Inborn Errors of Intermediary Metabolism in Pediatric Populations.Nutrients · 2023Review
- Predicting pharmaceutical inkjet printing outcomes using machine learning.International journal of pharmaceutics: X · 2023Article
- Application of artificial intelligence in 3D printing physical organ models.Materials today. Bio · 2023Review
- Prediction of drug permeation through microneedled skin by machine learning.Bioengineering & translational medicine · 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
4 authors.
Funding
Abstract
Microneedles (MNs) introduced a novel injection alternative to conventional needles, offering a decreased administration pain and phobia along with more efficient transdermal and intradermal drug delivery/sample collecting. 3D printing methods have emerged in the field of MNs for their time- and cost-efficient manufacturing. Tuning 3D printing parameters with artificial intelligence (AI), including machine learning (ML) and deep learning (DL), is an emerging multidisciplinary field for optimization of manufacturing biomedical devices. Herein, we presented an AI framework to assess and predict 3D-printed MN features. Biodegradable MNs were fabricated using fused deposition modeling (FDM) 3D printing technology followed by chemical etching to enhance their geometrical precision. DL was used for quality control and anomaly detection in the fabricated MNAs. Ten different MN designs and various etching exposure doses were used create a data library to train ML models for extraction of similarity metrics in order to predict new fabrication outcomes when the mentioned parameters were adjusted. The integration of AI-enabled prediction with 3D printed MNs will facilitate the development of new healthcare systems and advancement of MNs' biomedical applications.
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.