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T Engin

Publications and source records attributed to T Engin.

2 recordsLinked to original sources

Value of color Doppler sonography in the diagnosis of venous impotence.

The gold standard in the diagnosis of venous impotence is cavernosometry. But a less invasive and time-consuming screening test for patients with a high probability of venous impotence is required. Therefore we evaluated 82 men with erectile dysfunction by color Doppler sonography after intracavernosal injection of 60 mg papaverine. 28 of them who had a poor erectile response but sufficient arterial inflow were included in this study. 6 patients who were previously found to have psychogenic impotence formed the control group. After pharmacological stimulation by papaverine, all patients were examined for 20 min by color Doppler sonography in order to detect diastolic flow velocities in the cavernosal arteries and erectile responses. Both groups of patients underwent pharmaco-cavernosometry in a blind fashion afterwards. The diagnosis of venous impotence with color Doppler sonography was confirmed by dynamic pharmaco-cavernosometry in 25 of 28 (89%) and 6 of 6 (100%) subjects of the study and control groups, respectively. Statistically, in the diagnosis of venous dysfunction, color Doppler sonography had a sensitivity of 100%, specificity of 66.6%, accuracy rate of 91% and a positive predictive value of 0.892. The gold standard in the diagnosis of venous impotence is still cavernosometry, but it seems that color Doppler sonography may provide a sensitive assessment of penile venous competence.

Adult↗

Comparison of AR-based algorithms for respiratory sounds classification.

Respiratory sounds of pathological and healthy subjects were analyzed via autoregressive (AR) models with a view to construct a diagnostic aid based on auscultation. Using the AR vectors, two reference libraries, pathological and healthy, were built. Two classifiers, k-nearest neighbour (k-NN) classifier and a quadratic classifier, were designed and compared. Performances of the classifiers were tested for different model orders. The best classification results were obtained for model order 6.

Algorithms↗