Changing profile of infective endocarditis during a 20-year observation period.

BACKGROUND: Infective endocarditis (IE) is most often caused by bacteria. OBJECTIVES: The aim of this work is the research of the dynamics of the clinical laboratory and instrumental methods of the diagnostics during the period of two decades. METHODS: The data of 241 patients with infective endocarditis (IE) who were treated at the State Clinical Hospital named after Botkin S.P. was included in the research. 121 patients were observed from 2011 till 2020 (the first group) and 120 patients - from 1997 to 2004 (the second test group). These data included age and social structure of pathology, peculiarities of clinical picture, laboratory, and instrumental methods of research, as well as the outcome of the disease. We studied the concentrations of procalcitonin and presepsin in patients hospitalized after 2011. We observed pathomorphism of the modern IE. RESULTS: To discover the bacteriological origin of the disease, we found the diagnostic evaluation of inflammation, procalcitonin, and presepsin activities, using C-reactive protein, important. We observed decrease in the number of general and hospital deaths. CONCLUSIONS: The knowledge of the IE peculiarities during the IE progression is essential for timely diagnosis and more accurate pathology prediction (Fig. 5, Ref. 38). Text in PDF www.elis.sk Keywords: infectious endocarditis, valve apparatus disease, thromboembolic complications, immunocomplex complications, procalcitonin, presepsin.

Polyp detection with computer-aided diagnosis in white light colonoscopy: comparison of three different methods.

Background and study aims Detection of polyps during colonoscopy is essential for screening colorectal cancer and computer-aided-diagnosis (CAD) could be helpful for this objective. The goal of this study was to assess the efficacy of CAD in detection of polyps in video colonoscopy by using three methods we have proposed and applied for diagnosis of polyps in wireless capsule colonoscopy. Patients and methods Forty-two patients were included in the study, each one bearing one polyp. A dataset was generated with a total of 1680 polyp instances and 1360 frames of normal mucosa. We used three methods, that are all binary classifiers, labelling a frame as either containing a polyp or not. Two of the methods (Methods 1 and 2) are threshold-based and address the problem of polyp detection (i. e. separation between normal mucosa frames and polyp frames) and the problem of polyp localization (i. e. the ability to locate the polyp in a frame). The third method (Method 3) belongs to the class of machine learning methods and only addresses the polyp detection problem. The mathematical techniques underlying these three methods rely on appropriate fusion of information about the shape, color and texture content of the objects presented in the medical images. Results Regarding polyp localization, the best method is Method 1 with a sensitivity of 71.8 %. Comparing the performance of the three methods in the detection of polyps, independently of the precision in the location of the lesions, Method 3 stands out, achieving a sensitivity of 99.7 %, an accuracy of 91.1 %, and a specificity of 84.9 %. Conclusion CAD, using the three studied methods, showed good accuracy in the detection of polyps with white light colonoscopy.

Automated polyp detection in colon capsule endoscopy.

Colorectal polyps are important precursors to colon cancer, a major health problem. Colon capsule endoscopy is a safe and minimally invasive examination procedure, in which the images of the intestine are obtained via digital cameras on board of a small capsule ingested by a patient. The video sequence is then analyzed for the presence of polyps. We propose an algorithm that relieves the labor of a human operator analyzing the frames in the video sequence. The algorithm acts as a binary classifier, which labels the frame as either containing polyps or not, based on the geometrical analysis and the texture content of the frame.We assume that the polyps are characterized as protrusions that are mostly round in shape. Thus, a best fit ball radius is used as a decision parameter of the classifier. We present a statistical performance evaluation of our approach on a data set containing over 18 900 frames from the endoscopic video sequences of five adult patients. The algorithm achieves 47% sensitivity per frame and 81% sensitivity per polyp at a specificity level of 90%. On average, with a video sequence length of 3747 frames, only 367 false positive frames need to be inspected by an operator.