The possibility of injectable biomaterials being used in the therapy of peripheral artery disease (PAD) is investigated in this article. We conducted a thorough review of the literature on the use and efficacy of biomaterials (BMs) and drug-coated balloons (DCBs). These BMs included hydrogels, collagen scaffolds, and nanoparticles. These BMs could be used alone or in combination with growth factors, stem cells, or gene therapy. The treatment of peripheral artery disease with DCBs is increasingly common in the field of interventional angiology. Studies have been carried out to examine the effectiveness of paclitaxel-coated balloons such as PaccocathTM in lowering the frequency with which further revascularization operations are required. PCB angioplasty and angioplasty without paclitaxel did not significantly vary in terms of mortality, according to the findings of a recent meta-analysis that included the results of four randomized controlled studies. On the other hand, age was found to be a factor that predicted mortality. There was a correlation between the routine utilization of scoring balloon angioplasty along with DCBs and improved clinical outcomes in de novo lesions. In both preclinical and clinical testing, the SelutionTM DCB has demonstrated efficacy and safety, but further research is required to determine whether or not it is effective and safe over the long term. In addition, we reviewed the difficulties involved in bringing injectable BMs-based medicines to clinical trials, including the approval processes required by regulatory bodies. Injectable BMs have a significant amount of therapeutic promise for PAD, which highlights the need for more research and clinical studies to be conducted in this field. In conclusion, this research focuses on the potential of injectable BMs and DCBs in the treatment of PAD as well as the hurdles that must be overcome in order to translate these treatments into clinical trials. In this particular field, there is a demand for further research as well as clinical trials.

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Atherosclerotic plaque deposition within the coronary vessel wall leads to arterial stenosis and if not adequately treated, it may potentially have deteriorating consequences, such as a debilitating stroke, thus making early detection of the most importance. The manual plaque components annotation process is both time and resource consuming, therefore, an automatic and accurate segmentation tool is necessary. The main aim of this paper is to present the model for identification and segmentation of the atherosclerotic plaque components such as lipid core, fibrous and calcified tissue, by using Convolutional Neural Network on patch-based segments of ultrasound images. There was some research done on the topic of plaque components segmentation, but not in ultrasound imaging data. Due to the size of some plaque components being only a couple of millimeters, we argue that training a neural network on smaller image patches will perform better than a classifier based on the whole image. Besides the size of components, this decision is motivated by the observation that plaque components are not uniformly distributed throughout the whole carotid wall and that a locality-sensitive segmentation is likely to obtain better segmentation accuracy. Our model achieved good results in the segmentation of fibrous tissue but had difficulties in the segmentation of lipid and calcified tissue due to the quality of ultrasound images.