This paper concerns the analysis of the features obtained from thyroid ultrasound images in left and right transverse and longitudinal sections. In the image analysis, the thyroid lobe is treated as a texture for healthy subjects and patients with Hashimoto’s disease. The applied methods of analysis and image processing were profiled to obtain 10 features of the image. Then, their significance in the classification was shown. In this study, the examined group consisted of 29 healthy subjects aged 18 to 60 and 65 patients with Hashimoto's disease. For each subject, four ultrasound images were taken. They were all in transverse and longitudinal sections of the right and left lobe of the thyroid, which gave 376 images in total. 10 different features obtained from each ultrasound image were suggested. The analyzed thyroid lobe was marked automatically or manually with a rectangular element. The analysis of 10 features and the creation for each one of them their own decision tree configuration resulted in distinguishing 3 most significant features. The results of the quality of classification show accuracy above 94% for a non-trimmed decision tree.

This paper shows the influence of a measurement method of features in the diagnosis of Hashimoto’s disease. Sensitivity of the algorithm to changes in the parameters of the ROI, namely shift, resizing and rotation, has been presented. The obtained results were also compared to the methods known from the literature in which decision trees or average gray level thresholding are used. In the study, 288 images obtained from patients with Hashimoto’s disease and 236 images from healthy subjects have been analyzed. For each person, an ultrasound examination of the left and right thyroid lobe in transverse and longitudinal sections has been performed. With the use of the developed algorithm, a discriminant analysis has been conducted for the following five options: linear, diaglinear, quadratic, diagquadratic and mahalanobis. The left and right thyroid lobes have been analyzed both together and separately in transverse and longitudinal sections. In addition, the algorithm enabled to analyze specificity and sensitivity as well as the impact of sensitivity of ROI shift, repositioning and rotation on the measured features. The analysis has shown that the highest accuracy was obtained for the longitudinal section (LD) with the method of linear, yielding sensitivity = 76%, specificity = 95% and accuracy ACC = 84%. The conducted sensitivity assessment confirms that changes in the position and size of the ROI have little effect on sensitivity and specificity. The analysis of all cases, that is, images of the left and right thyroid lobes in transverse and longitudinal sections, has shown specificity ranging from 60% to 95% and sensitivity from 62% to 89%. Additionally, it was shown that the value of ACC for the method using decision trees as a classifier is equal to 84% for the analyzed data. Thresholding of average brightness of the ROI gave ACC equal to 76%.

Background and Objectives: Hypertrophic cardiomyopathy (HCM) depends on the primary impairment of sarcomeres, but it can also be associated with secondary alterations in the heart related to oxidative stress. The present study aimed to examine oxidative-antioxidant disturbances in patients with HCM compared with control individuals. Materials and Methods: We enrolled 52 consecutive HCM patients and 97 controls without HCM. The groups were matched for age, body mass index, and sex. Peripheral blood was collected from all patients to determine the total antioxidant capacity (TAC), total oxidant status (TOS), lipid hydroperoxide (LPH), and malondialdehyde (MDA). The oxidative stress index (OSI) was defined as the ratio of the TOS level to the TAC level. Results: The median age was 52 years, and 58.4% were female. The area under the curve (AUC) indicated good predictive power for the TAC and TOS [AUC 0.77 (0.69–0.84) and 0.83 (0.76–0.90), respectively], as well as excellent predictive power for the OSI [AUC 0.87 (0.81–0.93)] for HCM detection. Lipid peroxidation markers also demonstrated good predictive power to detect HCM patients [AUC_LPH = 0.73, AUC_MDA = 0.79]. Conclusions: The TOS, the TAC, LPH levels, and MDA levels have good predictive power for HCM detection. The holistic assessment of oxidative stress by the OSI had excellent power and could identify patients with HCM.