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The aim of this study was to evaluate whether textural analysis could differentiate between the two common types of lytic lesions imaged with use of radiography. Sixty-two patients were enrolled in the study with intraoral radiograph images and a histological reference study. Full textural analysis was performed using MaZda software. For over 10,000 features, logistic regression models were applied. Fragments containing lesion edges were characterized by significant correlation of structural information. Although the input images were stored using lossy compression and their scale was not preserved, the obtained results confirmed the possibility of distinguishing between cysts and granulomas with use of textural analysis of intraoral radiographs. It was shown that the important information distinguishing the aforementioned types of lesions is located at the edges and not within the lesion.

The present work is aimed at comparing the effectiveness of two different methods of risk factor assessment used for prediction of the CAD (coronary artery disease): the logistic regression method and the application of artificial neural networks. The former is widely used in medical research, while the latter is relatively rare. In the logistic regression method hierarchical analysis was employed to select the significant variables of the classification process. In the neural network approach several strategies were proposed for selection of the discriminative variables, all based on weight analysis in the constructed networks. Both methods have produced a consistent set of discriminative variables (Glu0, Ins0, Ins30, BMI, apoA1 and HDL-Ch), belonging to three groups of risk factors associated with insulin resistance, obesity and lipid disorders.