Machine Learning-Enabled Prediction of 3D-Printed Microneedle Features

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.

Nanocarrier-Integrated Microneedles: Divulging the Potential of Novel Frontiers for Fostering the Management of Skin Ailments

“…For the industrial-scale manufacturing of the MNs, novel technologies such as 3D printing and micromachining are exploited to reduce the cost and manufacturing variability . Manufacturing companies can utilize new-era technologies such as artificial intelligence (AI) and machine learning integrated with 3D printing to hasten the manufacturing of MNs with minimum variability. − Quality by design (QbD) and artificial neural network (ANN) approaches can also be used to optimize the process parameters to obtain reproducible results . Moreover, the programmable release pattern of the therapeutic entities from the MNs can be achieved by designing the structure of the MNs as well as the selection of polymer .…”

AI-Based Metamaterial Design

“…The healthcare field is undergoing a transition with the integration of AI and ML . With the growing population, disease monitoring, , tissue engineering, , and diagnostics , have become increasingly important to ensure public health and advance person-centered healthcare. The growing demand for accurate diagnostic technologies and health monitors can be met via AI-based design .…”

3D Printing of Dietary Products for the Management of Inborn Errors of Intermediary Metabolism in Pediatric Populations

“…Similarly, ML models have been used to predict the printability of pharma-inks in inkjet printing, enabling the adjustment of doses for patients exhibiting varying treatment responses [ 147 ]. Additionally, ML models have been developed for the design and fabrication of microneedles, showcasing AI’s role in predicting complex geometries for innovative medical devices [ 148 ].…”