Computer-aided diagnosis of carotid atherosclerosis based on ultrasound image statistics, laws' texture and neural networks

Quantitative characterisation of carotid atherosclerosis and classification into symptomatic or asymptomatic is crucial in planning optimal treatment of atheromatous plaque. The computer-aided diagnosis (CAD) system described in this paper can analyse ultrasound (US) images of carotid artery and classify them into symptomatic or asymptomatic based on their echogenicity characteristics. The CAD system consists of three modules: a) the feature extraction module, where first-order statistical (FOS) features and Laws' texture energy can be estimated, b) the dimensionality reduction module, where the number of features can be reduced using analysis of variance (ANOVA), and c) the classifier module consisting of a neural network (NN) trained by a novel hybrid method based on genetic algorithms (GAs) along with the back propagation algorithm. The hybrid method is able to select the most robust features, to adjust automatically the NN architecture and to optimise the classification performance. The performance is measured by the accuracy, sensitivity, specificity and the area under the receiver-operating characteristic (ROC) curve. The CAD design and development is based on images from 54 symptomatic and 54 asymptomatic plaques. This study demonstrates the ability of a CAD system based on US image analysis and a hybrid trained NN to identify atheromatous plaques at high risk of stroke.

Association of bone attrition with knee pain, stiffness and disability: a cross-sectional study

Bone pathologies as detected on MRI are associated with the presence of pain in knee osteoarthritis (OA). The authors examined whether bone attrition assessed on x-rays was associated with pain, stiffness and disability.

Does land-based exercise reduce pain and disability associated with hip osteoarthritis? A meta-analysis of randomized controlled trials

Objective To determine if clinical guidelines recommending therapeutic exercise for people with hip osteoarthritis (OA) are supported by rigorous scientific evidence. Methods A meta-analysis of randomized controlled trials (RCTs) recruiting people with hip OA and comparing some form of land-based exercise program (as opposed to exercises conducted in the water) with a non-exercise group in terms of hip pain and/or self-reported physical function. Results Thirty-two RCTs were identified, but only five met the inclusion criteria. Only one of the five included RCTs restricted recruitment to people with hip OA, the other four RCTs also recruiting participants with knee OA. The five included studies provided data on 204 and 187 hip OA participants for pain and physical function, respectively. Combining the results of the five included RCTs using a fixed-effects model demonstrated a small treatment effect for pain (standardized mean difference (SMD) −0.38; 95% confidence interval (CI) −0.67 to −0.09). No significant benefit in terms of improved self-reported physical function was detected (SMD −0.02; 95% CI −0.31 to 0.28). Conclusion Currently there is only silver level evidence (one small RCT) supporting the benefit of land-based therapeutic exercise for people with symptomatic hip OA in terms of reduced pain and improved physical function. The limited number and small sample size of the included RCTs restricts the confidence that can be attributed to these results.

Index of Complexity, Outcome and Need scored on plaster and digital models

The aim of this study was to compare standard plaster models with their digital counterparts for the applicability of the Index of Complexity, Outcome, and Need (ICON). Generated study models of 30 randomly selected patients: 30 pre- (T(0)) and 30 post- (T(1)) treatment. Two examiners, calibrated in the ICON, scored the digital and plaster models. The overall ICON scores were evaluated for reliability and reproducibility using kappa statistics and reliability coefficients. The values for reliability of the total and weighted ICON scores were generally high for the T(0) sample (range 0.83-0.95) but less high for the T(1) sample (range 0.55-0.85). Differences in total ICON score between plaster and digital models resulted in mostly statistically insignificant values (P values ranging from 0.07 to 0.19), except for observer 1 in the T(1) sample. No statistically different values were found for the total ICON score on either plaster or digital models. ICON scores performed on computer-based models appear to be as accurate and reliable as ICON scores on plaster models.