Expert opinion on best practice guidelines and competency framework for visual screening in children

PURPOSE: Screening programs to detect visual abnormalities in children vary among countries. The aim of this study is to describe experts' perception of best practice guidelines and competency framework for visual screening in children. METHODS: A qualitative focus group technique was applied during the Portuguese national orthoptic congress to obtain the perception of an expert panel of 5 orthoptists and 2 ophthalmologists with experience in visual screening for children (mean age 53.43 years, SD ± 9.40). The panel received in advance a script with the description of three tuning competencies dimensions (instrumental, systemic, and interpersonal) for visual screening. The session was recorded in video and audio. Qualitative data were analyzed using a categorical technique. RESULTS: According to experts' views, six tests (35.29%) have to be included in a visual screening: distance visual acuity test, cover test, bi-prism or 4/6(Δ) prism, fusion, ocular movements, and refraction. Screening should be performed according to the child age before and after 3 years of age (17.65%). The expert panel highlighted the influence of the professional experience in the application of a screening protocol (23.53%). They also showed concern about the false negatives control (23.53%). Instrumental competencies were the most cited (54.09%), followed by interpersonal (29.51%) and systemic (16.4%). CONCLUSIONS: Orthoptists should have professional experience before starting to apply a screening protocol. False negative results are a concern that has to be more thoroughly investigated. The proposed framework focuses on core competencies highlighted by the expert panel. Competencies programs could be important do develop better screening programs.

Reply to letter to the Editor: Expert opinion on best practice guidelines and competency framework for visual screening in children

The aim of this study was to describe experts’ perception of best-practice guidelines and competency framework for visual screening in children. This study uses qualitative data and shows individual/ group conceptualization. The use of evidence from qualitative studies has traditionally been a fundamental source of knowledge in the clinical and social sciences.

Value of a lower limb immobilization device for SPECT/CT image fusion optimization

The foot and the ankle are small structures commonly affected by disorders, and their complex anatomy represent significant diagnostic challenges. SPECT/CT Image fusion can provide missing anatomical and bone structure information to functional imaging, which is particularly useful to increase diagnosis certainty of bone pathology. However, due to SPECT acquisition duration, patient’s involuntary movements may lead to misalignment between SPECT and CT images. Patient motion can be reduced using a dedicated patient support. We aimed at designing an ankle and foot immobilizing device and measuring its efficacy at improving image fusion. Methods: We enrolled 20 patients undergoing distal lower-limb SPECT/CT of the ankle and the foot with and without a foot holder. The misalignment between SPECT and CT images was computed by manually measuring 14 fiducial markers chosen among anatomical landmarks also visible on bone scintigraphy. Analysis of variance was performed for statistical analysis. Results: The obtained absolute average difference without and with support was 5.1±5.2 mm (mean±SD) and 3.1±2.7 mm, respectively, which is significant (p<0.001). Conclusion: The introduction of the foot holder significantly decreases misalignment between SPECT and CT images, which may have clinical influence in the precise localization of foot and ankle pathology.

Robot visual localization through local feature fusion: an evaluation of multiple classifiers combination approaches

In the last decade, local image features have been widely used in robot visual localization. In order to assess image similarity, a strategy exploiting these features compares raw descriptors extracted from the current image with those in the models of places. This paper addresses the ensuing step in this process, where a combining function must be used to aggregate results and assign each place a score. Casting the problem in the multiple classifier systems framework, in this paper we compare several candidate combiners with respect to their performance in the visual localization task. For this evaluation, we selected the most popular methods in the class of non-trained combiners, namely the sum rule and product rule. A deeper insight into the potential of these combiners is provided through a discriminativity analysis involving the algebraic rules and two extensions of these methods: the threshold, as well as the weighted modifications. In addition, a voting method, previously used in robot visual localization, is assessed. Furthermore, we address the process of constructing a model of the environment by describing how the model granularity impacts upon performance. All combiners are tested on a visual localization task, carried out on a public dataset. It is experimentally demonstrated that the sum rule extensions globally achieve the best performance, confirming the general agreement on the robustness of this rule in other classification problems. The voting method, whilst competitive with the product rule in its standard form, is shown to be outperformed by its modified versions.