Miguel Coimbra

Abstract
Physiological signals are often corrupted by noisy sources. Usually, artificial intelligence algorithms analyze the whole signal, regardless of its varying quality. Instead, experienced cardiologists search for a high-quality signal segment, where more accurate conclusions can be draw. We propose a methodology that simultaneously selects the optimal processing region of a physiological signal and determines its decoding into a state sequence of physiologically meaningful events. Our approach comprises two phases. First, the training of a neural network that then enables the estimation of the state probability distribution of a signal sample. Second, the use of the neural network output within an integer program. The latter models the problem of finding a time window by maximizing a likelihood function defined by the user. Our method was tested and validated in two types of signals, the phonocardiogram and the electrocardiogram. In phonocardiogram and electrocardiogram segmentation tasks, the system's sensitivity increased on average from 95.1% to 97.5% and from 78.9% to 83.8%, respectively, when compared to standard approaches found in the literature.

Abstract

Abstract
Stomach cancer is the third deadliest type of cancer in the world (0.86 million deaths in 2017). In 2035, a 20% increase will be observed both in incidence and mortality due to demographic effects if no interventions are foreseen. Upper GI endoscopy (UGIE) plays a paramount role in early diagnosis and, therefore, improved survival rates. On the other hand, human and technical factors can contribute to misdiagnosis while performing UGIE. In this scenario, artificial intelligence (AI) has recently shown its potential in compensating for the pitfalls of UGIE, by leveraging deep learning architectures able to efficiently recognize endoscopic patterns from UGIE video data. This work presents a review of the current state-of-the-art algorithms in the application of AI to gastroscopy. It focuses specifically on the threefold tasks of assuring exam completeness (i.e., detecting the presence of blind spots) and assisting in the detection and characterization of clinical findings, both gastric precancerous conditions and neoplastic lesion changes. Early and promising results have already been obtained using well-known deep learning architectures for computer vision, but many algorithmic challenges remain in achieving the vision of AI-assisted UGIE. Future challenges in the roadmap for the effective integration of AI tools within the UGIE clinical practice are discussed, namely the adoption of more robust deep learning architectures and methods able to embed domain knowledge into image/video classifiers as well as the availability of large, annotated datasets.

Abstract

Abstract
The continuous growth of the world population and its agglomeration in urban cities, demand an increasing need for mobility, which in turn contributes to the worsening of traffic congestion and pollution in cities. Therefore, it is necessary to promote active travel, such as walking and cycling. However, this is not an easy task, as pedestrians and cyclists are the most vulnerable link in the system, and low levels of safety, security and comfort can contribute to choosing private cars over active travel. Hence, it is essential to understand the determinants that affect the perceptions of pedestrians and cyclists, in order to support the definition of policies that promote the use of active modes of transport. Thus, this article fills an important gap in the literature by identifying and discussing the objective and subjective determinants that affect the perceptions of safety, security and comfort of pedestrians and cyclists, through a systematic review of the literature published in the last ten years. It followed the PRISMA statement guidelines and checklist, resulting in 68 relevant articles that were carefully analyzed. The results show that the perception of safety is negatively affected by fear of traffic-related injuries, fear of falling related to infra-structure and infrastructure maintenance, and negative behavior of drivers. Regarding security, crime was the major concern of pedestrians and cyclists, either with emphasis on the person or on personal property. With regard to comfort, high levels of air and noise pollution, lack of vege-tation, bad weather conditions, slopes and long commuting distances negatively affected the users' perception. The results also suggest that poor lighting affects all domains, providing a negative perception of safety, security and comfort. Similarly, the presence of people is seen as negatively influencing the perception of safety and comfort, while the absence of people nega-tively impacts the perception of security. Therefore, the findings achieved by this study are key to assist in the definition of transport policies and infrastructure creation in large smart cities. Additionally, new transport policies are proposed and discussed.