Journal of

Background and Aim: Evaluation for STIs requires speculum examination. It is sometimes uncomfortable and rejected by many patients. Speculum examination often is impractical or not available in remote areas. Recently, it is possible to omit the speculum examination and noninvasively diagnose for Chlamydia and gonorrheal infections from urine sample. This comparison study was conducted by collected vaginal specimens directly without performing a speculum examination for the diagnosis of trichomonas infections.The aim of this study was comparison of two methods of vaginal discharge collection with and without speculum examination for diagnosis of the trichomonas infection.

Materials and Methods: We examined 100 patients with vaginal discharge to the gynecology clinic of the Taleghani hospital. Two vaginal swab were collected from vaginal discharge of patients before and during speculum examination for diagnosis of trichomoniasis. Both of vaginal specimens were tested with blinded microscopic. Compared collection methods sensitivities, specificity, positive predictive value, negative predictive value and accuracy of both methods was compared.

Results: Sensitivities, specificity, positive predictive value, negative predictive value and accuracy of speculum collection methods were 69%, 99%, 92% ,94% and 93% for trichomoniasis respectively. Sensitivities, specificity, positive predictive value, negative predictive value and accuracy of nonspeculum collection methods were 62.5%, 99%, 91%, 93% and 92% for trichomoniasis respectively. The differences between methods was not statistically significant (P= NS). There was a very good agreement between both methods for diagnosis of trichomoniasis (KAPPA= o.85).

Conclusions: We have demonstrated the benefit of  nonspeculum vaginal specimens for the diagnosis of trichomoniasis. This technique has applicability for studies involving the epidemiology of vaginal infection as well as for home diagnostic testing ,elder and pregnant women respectively.

Background and Aim: Artificial intelligence is a branch of computer science that has the ability of analyzing complex medical data. Using artificial intelligence is common in diagnosing, treating and taking care of patients. Warfarin is one of the most commonly prescribed oral anticoagulants. Determining the exact dose of warfarin needed for patients is one of the major challenges in the health system, which has attracted the attention of researchers. The purpose of this study was to determine the exact dose of warfarin needed for patients with artificial heart valves using artificial neural networks (ANN).
Materials and Methods: A total of 9 multi-layer perceptron ANNs with different structures were constructed and evaluated based on a dataset including 846 patients who had referred to the PT clinic in Tehran Heart Center in the second half of the year 2013. Finally, the best structure of ANN for warfarin dose was investigated. All simulations including data preprocessing and neural network designing were done in MATLAB environment.
Results: The effectiveness of ANNs was evaluated in terms of classification performance using 10-fold cross-validation procedure and the results showed that the best model was a network that had 7 neurons in its hidden layer with an average absolute error of 0.1, turbulence rate of 0.33, and regression of 0.87. 
Conclusion: The achieved results reveal that ANNs are able to predict warfarin dose in Iranian patients with an artificial heart valve. Although no system can be guaranteed to achieve 100% accuracy, they can be effective in reducing medical errors.