Intraoral radiographs have been taken to diagnose periapical lesions. Subsequent endodontic treatment needs to be evaluated quantitatively, that is often difficult due to various imaging factors as well as subjective visual interpretation. Therefore, we sought to establish an image analysis based quantitative model to evaluate endodontic treatments (40 effective and 43 noneffective cases). To normalize an image, the dentin area and the background were used as references. In each pair of images representing before and after treatment, the lesion area was manually selected by experts and segmented by tophat operation. Numerous features representing the effective bone healing were calculated. Using relative differences of selected features, an evaluation model was derived by logistic regression analysis. Gray level intensity and textural differences obtained from lesions significantly increased in the effectively treated cases. The model provided the accuracy of 80.7%. Our quantitative model may be helpful to evaluate endodontic treatment in clinical settings and in animal studies.
Atrial fibrillation (A-fib) is the most common cardiac arrhythmia. To effectively treat or prevent A-fib, automatic A-fib detection based on Electrocardiograph (ECG) monitoring is highly desirable. This paper reviews recently developed techniques for A-fib detection based on non-episodic surface ECG monitoring data. A-fib detection methods in the literature can be mainly classified into three categories: (1) time domain methods; (2) frequency domain methods; and (3) non-linear methods. In general the performances of these methods were evaluated in terms of sensitivity, specificity and overall detection accuracy on the datasets from the Physionet repository. Based on our survey, we conclude that no promising A-fib detection method that performs consistently well across various scenarios has been proposed yet.
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