Visual contrast response functions in Parkinson's disease: evidence from electroretinograms, visually evoked potentials and psychophysics

Objectives: Visual contrast detection thresholds and suprathreshold contrast discrimination thresholds were compared to luminance and flash/pattern electroretinograms (ERG) and visually evoked potentials (VEP) in patients with Parkinson's disease (n=31), patients with multiple system atrophy (n=6), patients with progressive supranuclear palsy (n=6) and control patients without central nervous disease (n=33). Methods: The stimuli were luminance modulated full-field (flash) or horizontally oriented sinewave gratings (pattern), the latter having either a low (0.5 cycles/deg) or medium (4.0 cycles/deg) spatial frequency. Stimulus contrast ranged from 10 to 80% so that contrast response functions could be derived. Results: Contrast thresholds were higher in the patients with Parkinson's disease than in the control patients. Contrast discrimination thresholds were also somewhat elevated in patients with Parkinson's disease. Pattern ERG amplitudes were significantly reduced in patients with Parkinson's disease for the medium spatial frequency stimulus, but less for the low spatial frequency and flash stimuli. Conclusions: Our results suggest that Parkinson’s disease impairs contrast processing in the retina. VEP amplitudes did not significantly differ between the groups for the conditions tested. Patients with progressive supranuclear palsy also showed impaired contrast perception and reduced ERG amplitudes, whereas patients with multiple system atrophy were less impaired

What Is a Decision Problem? Designing Alternatives

International audienceThis paper presents a general framework for the design of alternatives in decision problems. The paper addresses both the issue of how to design alternatives within "known decision spaces" and on how to perform the same action within "partially known or unknown decision spaces". The paper aims at providing archetypes for the design of algorithms supporting the generation of alternatives

Effectiveness of screening preschool children for amblyopia: a systematic review

<p>Abstract</p> <p>Background</p> <p>Amblyopia and amblyogenic factors like strabismus and refractive errors are the most common vision disorders in children. Although different studies suggest that preschool vision screening is associated with a reduced prevalence rate of amblyopia, the value of these programmes is the subject of a continuing scientific and health policy discussion. Therefore, this systematic review focuses on the question of whether screening for amblyopia in children up to the age of six years leads to better vision outcomes.</p> <p>Methods</p> <p>Ten bibliographic databases were searched for randomised controlled trials, non-randomised controlled trials and cohort studies with no limitations to a specific year of publication and language. The searches were supplemented by handsearching the bibliographies of included studies and reviews to identify articles not captured through our main search strategy.</p> <p>Results</p> <p>Five studies met the inclusion criteria. Of these, three studies suggested that screening is associated with an absolute reduction in the prevalence of amblyopia between 0.9% and 1.6% (relative reduction: between 45% and 62%). However, the studies showed weaknesses, limiting the validity and reliability of their findings. The main limitation was that studies with significant results considered only a proportion of the originally recruited children in their analysis. On the other hand, retrospective sample size calculation indicated that the power based on the cohort size was not sufficient to detect small changes between the groups. Outcome parameters such as quality of life or adverse effects of screening have not been adequately investigated in the literature currently available.</p> <p>Conclusion</p> <p>Population based preschool vision screening programmes cannot be sufficiently assessed by the literature currently available. However, it is most likely that the present systematic review contains the most detailed description of the main limitations in current available literature evaluating these programmes. Therefore, future research work should be guided by the findings of this publication.</p

Robust ordinal regression in preference learning and ranking

Multiple Criteria Decision Aiding (MCDA) offers a diversity of approaches designed for providing the decision maker (DM) with a recommendation concerning a set of alternatives (items, actions) evaluated from multiple points of view, called criteria. This paper aims at drawing attention of the Machine Learning (ML) community upon recent advances in a representative MCDA methodology, called Robust Ordinal Regression (ROR). ROR learns by examples in order to rank a set of alternatives, thus considering a similar problem as Preference Learning (ML-PL) does. However, ROR implements the interactive preference construction paradigm, which should be perceived as a mutual learning of the model and the DM. The paper clarifies the specific interpretation of the concept of preference learning adopted in ROR and MCDA, comparing it to the usual concept of preference learning considered within ML. This comparison concerns a structure of the considered problem, types of admitted preference information, a character of the employed preference models, ways of exploiting them, and techniques to arrive at a final ranking

Artificial intelligence extension of the OSCAR-IB criteria

Artificial intelligence (AI)-based diagnostic algorithms have achieved ambitious aims through automated image pattern recognition. For neurological disorders, this includes neurodegeneration and inflammation. Scalable imaging technology for big data in neurology is optical coherence tomography (OCT). We highlight that OCT changes observed in the retina, as a window to the brain, are small, requiring rigorous quality control pipelines. There are existing tools for this purpose. Firstly, there are human-led validated consensus quality control criteria (OSCAR-IB) for OCT. Secondly, these criteria are embedded into OCT reporting guidelines (APOSTEL). The use of the described annotation of failed OCT scans advances machine learning. This is illustrated through the present review of the advantages and disadvantages of AI-based applications to OCT data. The neurological conditions reviewed here for the use of big data include Alzheimer disease, stroke, multiple sclerosis (MS), Parkinson disease, and epilepsy. It is noted that while big data is relevant for AI, ownership is complex. For this reason, we also reached out to involve representatives from patient organizations and the public domain in addition to clinical and research centers. The evidence reviewed can be grouped in a five-point expansion of the OSCAR-IB criteria to embrace AI (OSCAR-AI). The review concludes by specific recommendations on how this can be achieved practically and in compliance with existing guidelines

Nurses' perceptions of aids and obstacles to the provision of optimal end of life care in ICU

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