Diabetes is a major, global and increasing condition that occurs when the insulin-glucagon regulatory mechanism is affected, leading to uncontrolled hyper- and hypoglycaemia events that may be life-threatening. However, it has been shown that through daily monitoring, appropriate patient-specific empowerment, lifestyle behavior of diabetics can be positively influenced and the associated and costly diabetes complications significantly reduced. As personal face-to-face coaching is costly and hard to scale, mobile applications and services have now become a key driver of mobile Health (mHealth) deployment, especially as a helpful way for self-management. <p> </p>Despite the huge mHealth market, a major limitation of many diabetes apps is that they do not use inputted data to help patients determine their daily insulin doses. On the other hand, the majority of existing insulin dose calculator apps provide no protection against - or even may actively contribute to - incorrect or inappropriate dose recommendations that put users at risk. Besides, there is clear evidence that lack of education on insulinotherapy and carbohydrate counting is associated with higher blood glucose variability with type 1 diabetes. Hence, there is a need for an accurate modelling of glucose-insulin dynamics together as well as providing adequate educational support. <p> </p>The aims of this paper are: a) to highlight the usefulness of mHealth technologies in chronic disease management; b) to describe and discuss the development of an insulin bolus calculator integrated into a pilot mHealth app; c) to underline the importance of diabetes self-management education.
Computer systems play an important role in medical imaging industry since radiologists depend on it for visualization,
interpretation, communication and archiving. In particular, computer-aided diagnosis (CAD) systems help in lesion
detection tasks. This paper presents the design and the development of an interactive segmentation tool for breast cancer
screening and diagnosis. The tool conception is based upon a user-centered approach in order to ensure that the
application is of real benefit to radiologists. The analysis of user expectations, workflow and decision-making practices
give rise to the need for an interactive reporting system based on the BIRADS, that would not only include the numerical
features extracted from the segmentation of the findings in a structured manner, but also support human relevance
feedback as well. This way, the numerical results from segmentation can be either validated by end-users or enhanced
thanks to domain-experts subjective interpretation. Such a domain-expert centered system requires the segmentation to
be sufficiently accurate and locally adapted, and the features to be carefully selected in order to best suit user's
knowledge and to be of use in enhancing segmentation. Improving segmentation accuracy with relevance feedback and
providing radiologists with a user-friendly interface to support image analysis are the contributions of this work. The
preliminary result is first the tool conception, and second the improvement of the segmentation precision.