In situ and in vitro comparison of laser fluorescence with visual inspection in detecting occlusal caries lesions

The aim of this study was to compare the in situ and in vitro performances of a laser fluorescence (LF) device (DIAGNOdent 2095) with visual inspection for the detection of occlusal caries in permanent teeth. Sixty-four sites were selected, and visual inspection and LF assessments were carried out, in vitro, three times by two independent examiners, with a 1-week interval between evaluations. Afterwards, the occlusal surfaces were mounted on the palatal portion of removable acrylic orthodontic appliances and placed in six volunteers. Assessments were repeated and validated by histological analysis of the tooth sections under a stereomicroscope. For both examiners, the highest intra-examiner values were observed for the visual inspection when in vitro and in situ evaluations were compared. The inter-examiner reproducibility varied from 0.61 to 0.64, except for the in vitro assessment using LF, which presented a lower value (0.43). The methods showed high specificity at the D(1) threshold (considering enamel and dentin caries as disease). In vitro evaluations showed the highest values of sensitivity for both methods when compared to the in situ evaluations at D(1) and D(2) (considering only dentinal caries as the disease) thresholds. For both methods, the results of sensitivity (at D(1) and D(2)) and accuracy (at D(1)) showed significant differences between in vitro and in situ conditions. However, the sensitivity (at D(1) and D(2)), specificity and accuracy (both at D(1)) of the methods were not significantly different when the same condition was considered. It can be concluded that visual inspection and LF showed better performance in vitro than in situ.

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.