Machine Learning Assists in the Design and Application of Microneedles
Source abstract
Abstract
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.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Advancements in Transdermal Drug Delivery · Dermatology and Skin Diseases · Skin Protection and Aging
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Guangdong Laboratory of Artificial Intelligence and Digital Economy (SZ), Shenzhen 518000, China
School of Biomedical Engineering, Marshall Laboratory of Biomedical Engineering, Shenzhen University Medical School, Shenzhen University, Shenzhen 518060, China
Institute of Chemical Materials, China Academy of Engineering Physics, Mianyang 621900, China
Shenzhen Key Laboratory of Nano-Biosensing Technology, Marshall Laboratory of Biomedical Engineering, International Health Science Innovation Center, Shenzhen University Medical School, Shenzhen University, Shenzhen 518060, China
Funding
National Key Research and Development Project · Shenzhen Science and Technology Innovation Commission · Shenzhen Science and Technology Program · Medical-Engineering Interdisciplinary Research Foundation of Shenzhen University
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