فهرست مطالب ali abbasian ardakani

Almost all medicine practices, including diagnosis, treatment, and prognosis of different diseases, need human attention to be precisely performed. As the number of parameters for medical decision-making increases, medical practices become error-prone, time-consuming, and cumbersome, leading to the quality degradation of decision-making procedures. This situation gets severe when most patients need to be considered in routine medical practices. As a science and engineering discipline, Artificial intelligence offers a wide variety of techniques to analyze various medical data with high accuracy and speed. Furthermore, the output of AI systems assists physicians as a second opinion in diagnosing the disease, optimal planning of the treatment procedure, and precise prediction of treatment response. Similar to many sub-fields of medicine, using AI techniques in the practice of urology is becoming prevalent. Therefore, various complex urological disorders can be diagnosed and treated; thanks to AI techniques. Therefore, great attention should be paid to the role of artificial intelligence in urology.

Cardiac echocardiography and cardiac ECG-gated single-photon emission computed tomography (SPECT) are the most common modalities for left ventricle (LV) volumes and function assessment. The temporal resolution of SPECT images is limited and an ECG provides better temporal resolution. This study investigates the impact of frame numbers on images in terms of qualitative and quantitative assessments.

In this study, 5 patients underwent echocardiography and cardiac ECG-gated SPECT imaging, and 5 standard views of the LV were recorded to determine LV walls boundaries and volumes. Also, 2 original images with 8 frames and 16 frames per cardiac cycle were recorded simultaneously in a single gantry orbit. Using the data extracted from the LV model, 8 extra new frames were created with interpolation between existing frames of the original 8-frame image. Three series of images (8 and 16 original and 16 interpolated) were reconstructed separately. LV volumes and ejection fraction (EF) were calculated using Quantitative Gated SPECT (QGS) software.

Compared to the original 8-frame gating, original 16-frame gated images resulted in larger end-diastole volume (EDV) (mean ± SD: 68.6 ± 27.11 mL vs 66.2±25.41 mL, p<0.001), smaller end-systole volume (ESV) (mean ± SD: 24.6±8.7 mL vs 26±7.3 mL, p<0.001), and higher EF (64% vs 60.2%, p<0.001). The results for the interpolated series were also different from the original images (closer to the original 16-frame series rather than 8-frame).

Changing the frame number from 8 to 16 in cardiac ECG-gated SPECT images caused a significant change in LV volumes and EF. Frame interpolation with sophisticated algorithms can be used to improve the temporal resolution of SPECT images.

During the past decade, coronary computed tomographic angiography (CCTA) has become the primary non-invasive imaging technique for the assessment of myocardial bridging (MB).
Objectivs: The aim of this study was to evaluate the ability of CCTA to predict myocardial ischemia in patients with MB.
Patients and A total of 32 MB patients (21 males and 11 females) participated in this study. Eleven MB parameters were measured to assess the ability of CCTA to predict MB patients with ischemia. In order to evaluate ischemia, all the patients underwent single positron emission computed tomography-myocardial perfusion imaging (SPECT-MPI) examination.
Ischemia was observed in 17 patients (53.1%), while 15 patients (46.9%) did not show signs of ischemia. Out of the 32 patients, superficial MB was observed in 15 patients while deep MB was identified in 12, and borderline was observed in five patients. All MB examined parameters were found to be significantly different between ischemic and non-ischemic patients, except for the location and tunnel artery diameter in diastole. Moreover, a cut-off value of 0.65 mm was able to discriminate ischemia with a sensitivity of 100%, specificity of 93%, and yield area under the receiver operating characteristic (ROC) curve (AUC) of 0.996. Also, by considering the depth cut-off value of 1.75 mm, ischemia can be distinguished with sensitivity and specificity of 100%. MB length had a lower discrimination power, with a cut off value of 22.5 mm yield, 76% sensitivity, 67% specificity, and AUC = 0.810 in the diagnosis of ischemia.
CCTA was a reliable modality with high accuracy to depict MB, identify high risk MB, and prevent unnecessary SPECT-MPI examination.

Early detection and reliable diagnosis of breast cancer could lead to improved cure rates and reduce mortality and management costs.
To explore the potential of texture analysis based on run-length matrix features for classifying benign and malignant breast tumors in ultrasound imaging.
A total of 70 breast tumors (38 benign and 32 malignant) have used in the proposed computer-aided diagnosis system. Twenty run-length matrix features have extracted for texture analysis in three normalizations (default, 3sigma, and 1% - 99%). Linear discriminant analysis and principal component analysis have employed to transform raw data to lower-dimensional spaces and increase discriminative power. The features have classified by the first nearest neighbor classifier.
The features under 3sigma normalization have designed via Linear discriminant analysis indicated high performance in classifying benign and malignant breast tumors with a sensitivity of 96.87%, specificity of 100%, accuracy of 98.57%, positive predictive value of 100%, and negative predictive value of 97.43%. The area under receiver operating characteristic curve was 0.992.
Run-length matrix features had a high potential to characterize and could help radiologist to diagnosis breast tumors.

Ultrasonography is the preferable imaging technique for monitoring and assessing complications in kidney allograft transplants. Computer-aided diagnostic system based on texture analysis in ultrasonographic imaging is recommended to identify changes in kidney function after allograft transplantation.
A total of 61 biopsy-proven kidney allograft recipients (11 rejected and 50 unrejected) were assessed by a computer-aided diagnostic system. Up to 270 statistical texture features were extracted as descriptors for each region of interest in each recipient. Correlations of texture features with serum creatinine level and differences between rejected and unrejected allografts were analyzed. An area under the receiver operating characteristic curve was calculated for each significant texture feature. Linear discriminant analysis was employed to analyze significant features and increase discriminative power. Recipients were classified by the first nearest neighbor classifier.
Fourteen texture features had a significant correlation with serum creatinine level and 16 were significantly different between the rejected and unrejected allografts, for which an area under the curve values were in the range of 0.575 for difference entropy S(4,0) to 0.676 for kurtosis. Using all 16 features, linear discriminant analysis indicated higher performance for classification of the two groups with an area under the curve of 0.975, which corresponded to a sensitivity of 90.9%, a specificity of 100%, a positive predictive value of 100%, and a negative predictive value of 98.0%.
Texture analysis was a reliable method, with the potential for characterization, and can help physicians to diagnose kidney failure after transplantation on ultrasonographic imaging.

Critical macromolecules of cells such as DNA are in exposure to damage of free radicals that induced from the interaction of ionizing radiation with biological systems. Selenium and vitamin-E are natural compounds that have been shown to be a direct free radical scavenger. The aim of this study was to investigate the radioprotective effect of selenium and vitamin-E separately and synergistically against genotoxicity induced by 6MV x-rays irradiation in blood lymphocytes.
Fifteen volunteers were divided into three groups include A, B and C. These groups were given selenium (800IU), vitamin-E (100mg) and selenium (400IU) vitamin-E (50mg), respectively. Peripheral blood samples were collected from each group before (0hr) and 1, 2 and 3hr after selenium and vitamin-E administration (separately and synergistically). Then the blood samples were irradiated to 200cGy of 6MV x-rays. After that lymphocyte samples were cultured with mitogenic stimulation to determine the chromosomal aberrations with micronucleus assay in cytokinesis-blocked binucleated cells.
The lymphocytes in the blood samples collected at one hr after ingestion selenium and vitamin-E, exposed in vitro to x-rays exhibited a significant decrease in the incidence of micronuclei, compared with control group at 0hr. The maximum protection and decrease in frequency of micronuclei (50%) were observed at one hr after administration of selenium and vitamin-E synergistically.
The data suggest that ingestion of selenium and vitamin-E as a radioprotector substance before exposures may reduce genetic damage caused by x-rays irradiation.

The aim of this study was to evaluate computer aided diagnosis (CAD) system with texture analysis (TA) to improve radiologists'' accuracy in identification of thyroid nodules as malignant or benign. A total of 70 cases (26 benign and 44 malignant) were analyzed in this study. We extracted up to 270 statistical texture features as a descriptor for each selected region of interests (ROIs) in three normalization schemes (default, 3s and 1%-99%). Then features by the lowest probability of classification error and average correlation coefficients (POE+ACC), and Fisher coefficient (Fisher) eliminated to 10 best and most effective features. These features were analyzed under standard and nonstandard states. For TA of the thyroid nodules, Principle Component Analysis (PCA), Linear Discriminant Analysis (LDA) and Non-Linear Discriminant Analysis (NDA) were applied. First Nearest-Neighbour (1-NN) classifier was performed for the features resulting from PCA and LDA. NDA features were classified by artificial neural network (A-NN). Receiver operating characteristic (ROC) curve analysis was used for examining the performance of TA methods. The best results were driven in 1-99% normalization with features extracted by POE+ACC algorithm and analyzed by NDA with the area under the ROC curve (Az) of 0.9722 which correspond to sensitivity of 94.45%, specificity of 100%, and accuracy of 97.14%. Our results indicate that TA is a reliable method, can provide useful information help radiologist in detection and classification of benign and malignant thyroid nodules.