Change detection is of paramount importance in medical imaging, serving as a non-invasive quantifiable powerful tool in diagnosis and in assessment of the outcome of treatment of tumors. We present a new quantitative method for detecting changes in volumetric medical data and in clustering of anatomical structures, based on assessment of volumetric distortions that are required in order to deform a test three-dimensional medical dataset segment onto its previously-acquired reference, or a given prototype in the case clustering.

Eye tracking is a non-invasive procedure to acquire eye-gaze data. The accuracy offered by the new eye tracking technologies gives to physicians and scientists a great opportunity to employ eye trackers to perform quantitative assessment of eye movements for diagnostic and rehabilitation purposes. However, eye trackers do not support physicians in their analysis, as they typically lack specific software solutions tailored to the diseases under investigation.

"Through this study we are presenting the initial steps towards a real time motion analysis system to predict the stability of carotid bifurcation plaques. The analysis is performed on B-mode video loops. Loops are analyzed in order to follow systoly and diastoly sections of the cardiac cycle and trace the motion of plaques during these periods. We had created a system that applies Farneback’s optical flow estimation method in order to estimate the flow between consecutive frames or frames at a predefined interval.

Spinal Cord Injury (SCI), along with disability, results in changes of brain organization and structure. While sensorimotor networks of patients and healthy individuals share similar patterns, unique functional interactions have been identified in SCI networks. Brain-Computer Interfaces (BCIs) have emerged as a promising technology for movement restoration and rehabilitation of SCI patients. We describe an experimental methodology to combine high-resolution electroencephalography (EEG) for investigation of functional connectivity following SCI and non-invasive BCI control of robotic arms.

Multiple Sclerosis (MS) is an unpredictable, often disabling disease that can adversely affect any body function; this often requires persons with MS to be active patients who are able to self-manage. There are currently thousands of health applications available but it is unknown how many concern MS. We conducted a systematic review of all MS apps present in the most popular app stores (iTunes and Google Play store) on June 2016 to identify all relevant MS apps. After discarding non-MS related apps and duplicates, only a total of 25 MS apps were identified.

Obesity is one of the major health risk factors behind the rise of non-communicable conditions. Understanding the factors influencing obesity is very complex since there are many variables that can affect the health behaviors leading to it. Nowadays, multiple data sources can be used to study health behaviors, such as wearable sensors for physical activity and sleep, social media, mobile and health data. In this paper we describe our experiences with the design of a dashboard for the visualization of actigraphy and biometric data from a childhood obesity camp in Qatar.

Breast cancer incidence and mortality rates vary geographically reflecting factors including regional and cultural differences in diet and lifestyle. There are numerous successful commercial mobile apps to help dieters control their diet and manage weight. However, such products are not suitable for people with special medical conditions that may require targeted dietary as well as motivational support.

As adults get older, the risks are increasing on their health, such as chronic diseases, functional decline and geriatric syndromes which threaten their well-being. Technology has many features to support aging wellness enabling older people maintaining healthy and sociable as they grow. However, the needs of the elderly of the population are not always the same. Stealth assessment, coming from the educational domain, can assess such needs.

Here we describe a new and effective real-time solution for detecting video segments showing an instrument used during diagnostic or therapeutic operations in endoscopic procedures. In addition, we present a new method to create training data: similarity-based data augmentation. This method automates most of the creation of a large training dataset and prevents extensive manual effort to collect and annotate training data by domain experts.

Various modalities are used for the examination of the gastrointestinal (GI) tract. One such modality is Wireless Capsule Endoscopy (WCE), a non- invasive technique which consists of a swallowable color camera that enables the detection of GI pathology with only minimal patient discomfort. Currently, tracking of the capsule position is estimated in the 3D abdominal space, using radio-frequency (RF) triangulation.