Machine Learning Assists in the Design and Application of Microneedles
Wenqing He,
Suixiu Kong,
Rumin Lin,
Yuanting Xie,
Shanshan Zheng,
Ziyu Yin,
Xin Huang,
Lei Su,
Xueji Zhang
Affiliations
Wenqing He
Guangdong Laboratory of Artificial Intelligence and Digital Economy (SZ), Shenzhen 518000, China
Suixiu Kong
Guangdong Laboratory of Artificial Intelligence and Digital Economy (SZ), Shenzhen 518000, China
Rumin Lin
Guangdong Laboratory of Artificial Intelligence and Digital Economy (SZ), Shenzhen 518000, China
Yuanting Xie
School of Biomedical Engineering, Marshall Laboratory of Biomedical Engineering, Shenzhen University Medical School, Shenzhen University, Shenzhen 518060, China
Shanshan Zheng
School of Biomedical Engineering, Marshall Laboratory of Biomedical Engineering, Shenzhen University Medical School, Shenzhen University, Shenzhen 518060, China
Ziyu Yin
School of Biomedical Engineering, Marshall Laboratory of Biomedical Engineering, Shenzhen University Medical School, Shenzhen University, Shenzhen 518060, China
Xin Huang
Institute of Chemical Materials, China Academy of Engineering Physics, Mianyang 621900, China
Lei Su
School of Biomedical Engineering, Marshall Laboratory of Biomedical Engineering, Shenzhen University Medical School, Shenzhen University, Shenzhen 518060, China
Xueji Zhang
Guangdong Laboratory of Artificial Intelligence and Digital Economy (SZ), Shenzhen 518000, China
Microneedles (MNs), characterized by their micron-sized sharp tips, can painlessly penetrate the skin and have shown significant potential in disease treatment and biosensing. With the development of artificial intelligence (AI), the design and application of MNs have experienced substantial innovation aided by machine learning (ML). This review begins with a brief introduction to the concept of ML and its current stage of development. Subsequently, the design principles and fabrication methods of MNs are explored, demonstrating the critical role of ML in optimizing their design and preparation. Integration between ML and the applications of MNs in therapy and sensing were further discussed. Finally, we outline the challenges and prospects of machine learning-assisted MN technology, aiming to advance its practical application and development in the field of smart diagnosis and treatment.
