Breast cancer was the most diagnosed cancer in 2020. Several thousand women continue to die from this disease. A better and earlier diagnosis may be of great importance to improving prognosis, and that is where Artificial Intelligence (AI) could play a major role. This paper surveys different applications of AI in Breast Imaging. First, traditional Machine Learning and Deep Learning methods that can detect the presence of a lesion and classify it into benign/malignant—which could be important to diminish reading time and improve accuracy—are analyzed. Following that, researches in the field of breast cancer risk prediction using mammograms—which may be able to allow screening programs customization both on periodicity and modality—are reviewed. The subsequent section analyzes different applications of augmentation techniques that allow to surpass the lack of labeled data. Finally, still concerning the absence of big datasets with labeled data, the last section studies Self-Supervised learning, where AI models are able to learn a representation of the input by themselves. This review gives a general view of what AI can give in the field of Breast Imaging, discussing not only its potential but also the challenges that still have to be overcome.

Breast cancer affects thousands of women across the world, every year. Methods to predict risk of breast cancer, or to stratify women in different risk levels, could help to achieve an early diagnosis, and consequently a reduction of mortality. This paper aims to review articles that extracted texture features from mammograms and used those features along with machine learning algorithms to assess breast cancer risk. Besides that, deep learning methodologies that aimed for the same goal were also reviewed. In this work, first, a brief introduction to breast cancer statistics and screening programs is presented; after that, research done in the field of breast cancer risk assessment are analyzed, in terms of both methodologies used and results obtained. Finally, considerations about the analyzed papers are conducted. The results of this review allow to conclude that both machine and deep learning methodologies provide promising results in the field of risk analysis, either in a stratification in risk groups, or in a prediction of a risk score. Although promising, future endeavors in this field should consider the possibility of the implementation of the methodology in clinical practice.

Higher education institutions (HEIs) are favored environments for the implementation of technological solutions that accelerate the generation of smart campi, given the dynamic ecosystem they create based on the involvement of inspired and motivated human resources (students, professors, and researchers), moving around in an atmosphere of advanced digital infrastructures and services. Moreover, HEIs have, in their mission, not only the creation of integrated knowledge through Research and Development (R&D) activities but also solving societal problems that address the academic community expectations concerning environmental issues, contributing, therefore, towards a greener society embodied within the United Nations (UN) Sustainable Development Goals (SDGs). This article addresses the design and implementation of a Smartbottle Ecosystem in which an interactive and reusable water bottle communicates with an intelligent water refill station, both integrated by the Internet of Things (IoT) and Information and Communications Technologies (ICT), to eliminate the use of single-use plastic water bottles in the premises of the Polytechnical Institute of Viana do Castelo (IPVC), an HEI with nearly 6000 students. Three main contributions were identified in this research: (i) the proposal of a novel methodology based on the association of Design Thinking and Participatory Design as the basis for Sustainable Design; (ii) the design and development of an IoT-enabled smartbottle prototype; and (iii) the usability evaluation of the proposed prototype. The adopted methodology is rooted in Design Thinking and mixes it with a Participatory Design approach, including the end-user opinion throughout the Smartbottle Ecosystem design process, not only for the product design requirements but also for its specification. By promoting a participatory solution tailored to the IPVC academic community, recycled plastic has been identified as the preferential material and a marine mammal was selected for the smartbottle shape, in the process of developing a solution to replace the single-use plastic bottles.

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