Software as medical devices (SaMDs) have become part of clinical practice and the management of the development and control processes of the documentation associated with them are an integral part of many medical realities. The European Regulation, MDR (EU) 2017/745, introduces a classification rule (rule 11, Annex VIII) specifically for software, which provides more explicit requirements than in the past, leading to classification of many software to higher risk and therefore to more complex certification processes. In this context, planning and awareness of possible regulatory strategies and related standards are fundamental for the key stakeholders, but this complex landscape can be perceived as fragmented. The aim of this work is to provide an amalgamated overview of how the current EU normative framework integrates into the various phases of the life-cycle of a medical device software, trying to ensure its safe and effective development. In addition to the MDR, the main normative references relevant to the medical device software sector were taken into consideration. Specifically, the IEC 62304 standard clarifies the main processes of the software life-cycle, including the analysis of problems and changes, and the IEC 82304 standard completes its management by addressing activities relating to post-market phases and requirements. In addition, the various steps include also key points such as risk identification and control (ISO 14971), design, implementation and validation of usability requirements (IEC 62366) and in general the quality of the context in which the software is developed and maintained (ISO 13485). The application of these standards can support the activities of the various stakeholders and facilitate evidence of compliance with the regulatory requirements by MDR. Based on the software life cycle, a mapping of the requirements from the entire normative framework analyzed over the various phases was implemented. A detailed and integrated picture of the regulatory context behind the life cycle of a SaMD has been provided: this can facilitate the implementation of a balanced and effective approach, including key aspects, such as risk management and usability processes, and ensuring safety for the end user.

Background: Atherosclerotic disease is the leading global cause of death, driven by progressive plaque accumulation in the arteries. Ultrasound (US) imaging, both conventional (CUS) and intravascular (IVUS), is crucial for the non-invasive assessment of atherosclerotic plaques. Deep learning (DL) techniques have recently gained attention as tools to improve the accuracy and efficiency of image analysis in this domain. This paper reviews recent advancements in DL-based methods for the segmentation, classification, and quantification of atherosclerotic plaques in US imaging, focusing on their performance, clinical relevance, and translational challenges. Methods: A systematic literature search was conducted in the PubMed, Scopus, and Web of Science databases, following PRISMA guidelines. The review included peer-reviewed original articles published up to 31 January 2025 that applied DL models for plaque segmentation, characterization, and/or quantification in US images. Results: A total of 53 studies were included, with 72% focusing on carotid CUS and 28% on coronary IVUS. DL architectures, such as UNet and attention-based networks, were commonly used, achieving high segmentation accuracy with average Dice similarity coefficients of around 84%. Many models provided reliable quantitative outputs (such as total plaque area, plaque burden, and stenosis severity index) with correlation coefficients often exceeding R = 0.9 compared to manual annotations. Limitations included the scarcity of large, annotated, and publicly available datasets; the lack of external validation; and the limited availability of open-source code. Conclusions: DL-based approaches show considerable promise for advancing atherosclerotic plaque analysis in US imaging. To facilitate broader clinical adoption, future research should prioritize methodological standardization, external validation, data and code sharing, and integrating 3D US technologies.

Background: Wearable ultrasound (US) is an emerging innovative approach with a possibly huge impact on personalized medicine. Recent advancements in transducer material and architecture provide new opportunities in many medical areas. Within this context, assessment of vascular aging can play a crucial role especially related to the early detection of vascular alterations, i.e., before disease-related symptoms occur, reshaping the concept of cardiovascular prevention. Aim: Within this work, we aim to stimulate a multidisciplinary discussion about the possible use of wearable ultrasound in the vascular aging field. Methods: An overview on wearable ultrasound and its potential in the vascular aging field are provided with a view on data processing workflow, preclinical applications, clinical impact, and industrial challenges. Results and conclusions: The concept of wearability opens interesting scenarios for a more effective adoption of the ultrasound technology that is currently underutilized. However, there are still several open issues, both in terms of safety and performance that need to be addressed to translate the innovation into clinical practice. The combination of innovative wearable devices with the holistic and versatile approach typical for US imaging has the potential to revolutionize the vascular field and a multidisciplinary discussion about this challenge can support its advancement.