Ischaemic brain changes associated with catheter-based diagnostic cerebral angiography : a diffusion-weighted imaging study

Purpose: This study aims to evaluate the incidence of clinically silent embolic cerebral infarctions and associated risk factors following diagnostic cerebral angiography with diffusion-weighted imaging (DWI). Material and methods: A total of 71 cerebral digital subtraction angiograms (42 male, 29 female, average age: 56.0 ± 15.0) obtained using nonionic contrast material were prospectively evaluated. To assess embolic events, before and after (1-3 days) angiography, DWI was performed. The risk factors for embolic ischemic brain changes such as the patient's age and sex, atherosclerotic vessel wall disease, type of indication for catheter angiography, the number and size of the catheters, anatomic variants, selective/nonselective catheterization, contrast media volume, and time of procedure were determined. Fisher's exact tests and Student t-tests were used for the statistical analyses of outcomes. Results: Thirteen new silent ischemic lesions were identified in 7 out of 71 patients who underwent diagnostic cerebral angiography. Embolic cerebral lesions were often 6-10mm in diameter. According to the findings in this study, there was a strong correlation between diffusion abnormality and patient age, which was considered risk factors (p 0.05). Conclusions: In elderly patients, the angiographic procedures should be performed meticulously and DWI in all patients obtained routinely, even if the regular neurological examination shows they are healthy. In this way, the presence of microemboli and clinical results can be evaluated

Diffusion-weighted MRI and FLAIR sequence for differentiation of hydatid cysts and simple cysts in the liver

Purpose: The contribution of DWI and FLAIR to the differential diagnosis of type 1, 2, and 3 hydatid cysts and simple liver cysts was investigated according to the Gharbi classification. This study is the first report using FLAIR sequence for the differential diagnosis of liver hydatid cysts in this regard. Methods: A total of 82 hydatid cysts and 40 simple cysts were scanned with DWI (in b600-b1000 values) and FLAIR sequence. In 64 patients included in the study, a total of 122 cystic lesions were diagnosed histopatho-logically or during follow-up. FLAIR and DWI signal characteristics were evaluated, and ADC values were calculated. Results: The mean ADC value of hydatid cysts on DWI (b600) was 3.07 +/- 0.41 x 10(-3) s/mm(2), while it was 3.91 +/- 0.51 x 10(-3) s/mm(2) for simple cysts and the difference was statistically significant (p < 0.05). On b1000 DWI, the mean ADC values of hydatid and simple cysts were 2.99 +/- 0.38 x 10(-3) s/mm(2) and 3.43 +/- 0:29 x 10(-3) s/mm(2), respectively (p < 0.05). The qualitative evaluation of the signal intensity on b600-1000 DWI demonstrated the difference between the simple and hydatid cyst groups (p < 0.05). Type 2 hydatid cysts alone were distinguished from type 2-3 hydatid and simple cysts by FLAIR (p < 0.05). Conclusions: ADC values can distinguish between hydatid cyst and simple cyst. FLAIR contributes to the differ-entiation of type 2 hydatid and simple cysts

Detection of breast cancer via deep convolution neural networks using MRI images

Ikizceli, Turkan/0000-0002-5683-0391; Erbay, Hasan/0000-0002-7555-541X; YURTTAKAL, Ahmet Hasim/0000-0001-5170-6466WOS:000538675900065Breast cancer is the type of cancer that develops from cells in the breast tissue. It is the leading cancer in women. Early detection of the breast cancer tumor is crucial in the treatment process. Mammography is a valuable tool for identifying breast cancer in the early phase before physical symptoms develop. To reduce false-negative diagnosis in mammography, a biopsy is recommended for lesions with greater than a 2% chance of having suspected malignant tumors and, among them, less than 30 percent are found to have malignancy. To decrease unnecessary biopsies, recently, Magnetic Resonance Imaging (MRI) has also been used to diagnose breast cancer. MRI is the highly recommended test for detecting and monitoring breast cancer tumors and interpreting lesioned regions since it has an excellent capability for soft tissue imaging. However, it requires an experienced radiologist and time-consuming process. On the other hand, convolutional neural networks (CNNs) have demonstrated better performance in image classification compared to feature-based methods and show promising performance in medical imaging. Herein, CNN was employed to characterize lesions as malignant or benign tumors using MRI images. Using only pixel information, a multi-layer CNN architecture with online data augmentation was designed. Later, the CNN architecture was trained and tested. The accuracy of the network is 98.33% and the error rate 0.0167. The sensitivity of the network is 1.0 whereas specificity is 0.9688. The precision is 0.9655

Transition from pandemic to infodemic: an analysis of Turkish-language COVID-19 YouTube videos

Background: YouTube can be a powerful educational tool for the dissemination of health information. However, if uploaded health-related videos are inaccurate, it can mislead, create confusion and generate panic. Aims: This study aimed to determine the success of the most-watched Turkish-language COVID-19 YouTube videos regarding information and guidance on the disease for the public. The secondary aim of this study was to evaluate the accuracy and quality of such video content