Background Bone age in children is mainly assessed using the Greulich and Pyle (GP) atlas, a validated method with limited interobserver accuracy. While automated methods increase interobserver accuracy, they represent considerable costs and technical requirements. Objective A proof-of-concept study to create and evaluate an online software program, Boneureka © , based on linear metacarpal length measurements, to assess bone age in healthy children. Materials and methodsThe study retrospectively included 434 consecutive children (215 girls) who underwent a lefthand radiograph to rule out trauma between March 2008 and December 2017. Two reviewers measured the second to fourth metacarpal lengths on each radiograph and the distance between the centre of the epiphyses of the second and fifth metacarpals. A single reviewer estimated the bone age using the GP atlas. The automated software assessed the bone age for all radiographs. A mathematical model was developed based on linear regressions to provide the mean bone age and standard deviation based on the estimates. Pearson and intraclass correlation coefficient (ICC) were used to evaluate the correlation and agreement between the estimated bone ages using Boneureka © , the GP atlas and BoneXpert ® compared to chronological age. ResultsThe measure that showed the highest correlation (r 2 =0.877 for girls and r 2 =0.834 for boys; P<.001) and the highest ICC (ICC=0.937 for girls and ICC=0.926 for boys; P<0.001) with chronological age was length of the second metacarpal. The GP atlas and the automated software evaluation had excellent ICC with chronological age (ICC>0.95 for both methods and sexes). Using this data, we created an online software program based on the second metacarpal length to obtain bone age estimates, means and standard deviations. ConclusionThe newly created online software Boneureka, © based on the second metacarpal length, is a reliable and userfriendly tool to assess bone age in healthy children. Further studies on a larger population should be performed to validate the developed reference values.
This article analyze the results of the italian municipal elections held in May 2011. First we make simple count of the municipalities won by varius political blocs, secondly we make a comparison with the results of regional elections of 2010. We have conpared data concerning both the performances of political blocks and those of the political parties who appeared in this elections. We also presents the results of disaggregated data, both from the demographics standpoint and from geographical point view. The analysis shows a clear electoraldefeat of the center-right coalition both in terms of municipalities lost and in terms of percentage of votes obtained. The fact that these two phenomena have occured especially in the North, its traditional area of electoral strenght, make this defeat particulary significant. The centre left coalition, due the difficulties of itsopponent, gets a good results in terms of number of municipalities won, while not improving itsperformance in therm of percentages of votes obtained. The centrist coalition, finally, does not goet get agreat performance in terms of votes obtained, but it often proved decisive in forcing the other twoconditions to the second ballot.
BackgroundIn this study, we aimed to compare two outbreaks of coronavirus disease 2019 in Belgium in tomographic and biological-clinical aspects with artificial intelligence (AI). MethodologyWe performed an observational retrospective study. Adult patients who were symptomatic in the first seven days with COVID-19 infection, diagnosed by chest computed tomography (CT) and/or reverse transcriptionpolymerase chain reaction, were included in this study. The first wave of the pandemic lasted from March 25, 2020, to May 25, 2020, and the second wave lasted from October 7, 2020, to December 7, 2020. For each wave, two subgroups were defined depending on whether respiratory failure occurred during the course of the disease. The quantitative estimation of COVID-19 lung lesions was performed by AI, radiologists, and radiology residents. The chest CT severity score was calculated by AI. ResultsIn the 202 patients included in this study, we found statistically significant differences for obesity, hypertension, and asthma. The differences were predominant in the second wave. Moreover, a mixed distribution (central and peripherical) of pulmonary lesions was noted in the second wave, but no differences were noted regarding mortality, respiratory failure, complications, and other radiological and biological elements. Chest CT severity score was among the risk factors of mortality and respiratory failure. There was a mild agreement between AI and visual evaluation of pulmonary lesion extension (K = 0.4). ConclusionsBetween March and December 2020, in our cohort, for the majority of the parameters analyzed, we did not record significant changes between the two waves. AI can reduce the experience and performance gap of radiologists and better establish a hospitalization criterion.
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