Abstract
In daily medical practices intravenous (IV) catheterization is performed on the majority of patients to inject medication or to get blood samples. Patients whose veins are not visible require multiple attempts in order to insert the catheter accurately. Different techniques have been proposed and a few are in use at present to localize the veins in order to avoid the wrong catheterization and reduce the number of attempts. In literature, numerous imaging techniques can be found for subcutaneous veins localization. These techniques have dissimilar methods and applications. The aim of this paper is to present a review on the most commonly used techniques amongst all of them; these are Transillumination, Photoacoustic and near infrared (NIR) imaging. The basic working principle along with evolution of these techniques has been stated from the current state-of-the-art. Advantages and limitations are underlined in terms of effectiveness, simplicity in usage, portability and cost effectiveness.
Keywords: Intravenous catheterization, subcutaneous veins, imaging, near infrared (NIR), transillumination, photoacoustic.
Call for Papers in Thematic Issues
Intelligent Information Retrieval for Multispectral and Hyperspectral Imaging
Multispectral and hyperspectral imaging is an interdisciplinary research area, widely focuses on spectral, spatial, and temporal data. It offers a plethora of opportunities to efficiently analyze the vast area of the earth's surface. However, there are numerous factors such as dimensionality and size of the hyperspectral data, lesser training samples, ...read more
Machine and Deep Learning in Medical Image Diagnosis
Medical Image Processing regards a set of methodologies that have been developed over recent years with the purpose of improving medical image quality, improving medical data visualization, understanding, and assisting medical diagnosis, and so on. The application of machine and deep learning methods in sensing and imaging can potentially have ...read more
Quantum Machine Learning for Medical Data and Imaging
Quantum computing has promised a significant speedup in certain computationally intensive tasks that are intractable on classical computers. Researchers in quantum machine learning, such as those working in computer vision, image processing, biomedical analysis, and related topics, may play an important role in comprehending and working on complicated medical data, ...read more
19