The emergence of microneedle arrays (MNAs) as a novel, simple, and minimally invasive administration approach largely addresses the challenges of traditional drug delivery. In particular, the dissolvable MNAs act as a promising, multifarious, and well-controlled platform for micro-nanotransport in medical research and cosmetic formulation applications. The effective delivery mostly depends on the behavior of the MNAs penetrated into the body, and accurate assessment is urgently needed. Advanced imaging technologies offer high sensitivity and resolution visualization of cross-scale, multidimensional, and multiparameter information, which can be used as an important aid for the evaluation and development of new MNAs. The combination of MNA technology and imaging can generate considerable new knowledge in a cost-effective manner with regards to the pharmacokinetics and bioavailability of active substances for the treatment of various diseases. In addition, noninvasive imaging techniques allow rapid, receptive assessment of transdermal penetration and drug deposition in various tissues, which could greatly facilitate the translation of experimental MNAs into clinical application. Relying on the recent promising development of bioimaging, this review is aimed at summarizing the current status, challenges, and future perspective on in vivo assessment of MNA drug delivery by various imaging technologies.
Intravenous cannulation is the most important phase in medical practices. Currently, limited literature is available about visibility of veins and the characteristics of patients associated with difficult intravenous access. In modern medical treatment, a major challenge is locating veins for patients who have difficult venous access. Presently, some products of vein locators are available in the market to improve vein access, but they need auxiliary equipment such as near infrared (NIR) illumination and camera, which add weight and cost to the devices, and cause inconveniences to daily medical care. In this paper, a vein visualization algorithm based on the deep learning method was proposed. Based on a group of synchronous RGB/NIR arm images, a convolutional neural network (CNN) model was designed to implement the mapping from RGB to NIR images, where veins can be detected from skin. The model has a simple structure and less optimization parameters. A color transfer scheme was also proposed to make the network adaptive to the images taken by smartphone in daily medical treatments. Comprehensive experiments were conducted on three datasets to evaluate the proposed method. Subjective and objective evaluations showed the effectiveness of the proposed method. These results indicated that the deep learning-based method can be used for visualizing veins in medical care applications.
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