[Meningitis (I)--differential diagnosis; aseptic and chronic meningitis].

Meningitis is the most common serious manifestation of infection of the central nervous system. Inflammatory involvement of the subarachnoid space with meningeal irritation leads to the classical triad of headache, fever, and meningism, and to a pleocytosis of the cerebrospinal fluid (CSF). Meningitis is clinically categorized into an acute and chronic disease based on the acuity of symptoms. Acute meningitis develops over hours to days, while in chronic meningitis symptoms evolve over days or even weeks. Aseptic meningitis, in which no bacterial pathogen can be isolated by routine cultures, can mimic bacterial meningitis, but the disease has a much more favorable prognosis. Many cases of aseptic meningitis are caused by viruses, primarily enteroviruses, but bacteria and noninfectious etiologies also cause meningitis with negative cultures. Symptoms of meningeal inflammation with CSF pleocytosis that persist for more than 4 weeks define the chronic meningitis syndrome. The diagnosis is based on the patient history, clinical evidence of meningitis, CSF examination, and often imaging studies. The differential diagnosis is broad, and the predominant CSF cell type can provide clues as to the underlying disease. Empiric therapy is primarily based on the age of the patient, with modifications if there are positive findings on CSF gram stain or if the patient presents with special risk factors. In patients with chronic meningitis, a definite diagnosis is often not available or delayed for days, in which case empiric therapy may have to be initiated. It is important to cover the treatable causes of meningitis, for which the outcome is poor if treatment is delayed.

Food Volume Computation for Self Dietary Assessment Applications

There is great demand for easily-accessible, user-friendly dietary self-management applications. Yet accurate, fully-automatic estimation of nutritional intake using computer vision methods remains an open research problem. One key element of this problem is the volume estimation, which can be computed from 3D models obtained using multi-view geometry. The paper presents a computational system for volume estimation based on the processing of two meal images. A 3D model of the served meal is reconstructed using the acquired images and the volume is computed from the shape. The algorithm was tested on food models (dummy foods) with known volume and on real served food. Volume accuracy was in the order of 90 %, while the total execution time was below 15 seconds per image pair. The proposed system combines simple and computational affordable methods for 3D reconstruction, remained stable throughout the experiments, operates in near real time, and places minimum constraints on users.

Data-driven healthcare: from patterns to actions

The era of big data opens up new opportunities in personalised medicine, preventive care, chronic disease management and in telemonitoring and managing of patients with implanted devices. The rich data accumulating within online services and internet companies provide a microscope to study human behaviour at scale, and to ask completely new questions about the interplay between behavioural patterns and health. In this paper, we shed light on a particular aspect of data-driven healthcare: autonomous decision-making. We first look at three examples where we can expect data-driven decisions to be taken autonomously by technology, with no or limited human intervention. We then discuss some of the technical and practical challenges that can be expected, and sketch the research agenda to address them.