Ryu, Jegyeong Kim, Jiyeop Park, Chan Wook Kim, Hae­Yoon Lee, Deuk hee Han, Amy Kyungwon 2025-08-26T02:30:50Z 2025-08-26T02:30:50Z 2025-08-25 2025-10 1438-1656 https://pubs.kist.re.kr/handle/201004/153040 Microneedles offer a minimally invasive and painless approach to drug delivery, enhancing therapeutic efficacy while minimizing side effects. Despite extensive efforts to minimize tissue damage and pain during microneedle insertion, identifying an optimal shape that simultaneously enhances tissue anchoring and reduces insertion force remains challenging, as anchoring typically relies on barbed features that inherently increase tissue damage. This study presents a machine learning-based methodology for optimizing microneedle shapes, combining Bayesian optimization and finite element methods to develop novel, low-pain, low-damage, and improved tissue-anchoring microneedle designs. Specifically, the study focuses on optimizing designs that are readily manufacturable via 3D printing to ensure scalable and cost-efficient production. Compared to conventional cone-shaped microneedles, the optimal design increases the pull-out-to-penetration energy ratio by 5.2-fold, reflecting enhanced tissue anchoring and reduced tissue damage. For validation, we conducted experiments and confirmed a 6.0-fold improvement in PPR in the optimized microneedle design. By incorporating application-specific modifications, this approach has the potential to generate diverse, minimal pain microneedle designs tailored to a variety of medical applications. English John Wiley & Sons Ltd. Machine Learning-Based Design of 3D-Printable Microneedles for Enhanced Tissue Anchoring with Reduced Tissue Damage Article 10.1002/adem.202501165 1 Advanced Engineering Materials, v.27, no.20 Advanced Engineering Materials 27 20 N scie scopus 001598838100035 Materials Science, Multidisciplinary Materials Science Article NEEDLE PENETRATION FABRICATION FORCE ARRAY SKIN INSERTION Bayesian optimization finite element analysis machine learning microneedle optimal design tissue anchoring 3D printing