Quantitative radial force measurements of Woven EndoBridge devices.

BACKGROUND: Lateral/radial forces and the mechanical properties of Woven EndoBridge (WEB) devices have significant importance for therapeutic success. In other words, adequate apposition of the lateral wall of a cerebral aneurysm is critical for preventing recurrence or re-rupture risk. OBJECTIVE: This study aimed to investigate the pressure values applied by different WEB devices to the lateral walls of aneurysms and the relationships between these pressure measurements and the diameters of WEB devices. METHODS: By placing four WEB devices of different sizes and types between two rigid metal plates, the lateral forces applied by these WEB devices to plates of different apertures were measured quantitatively. We tested a single device of each size over multiple periods. The total number of examined WEB devices is four. RESULTS: There was a significant negative relationship between plate distances and pressure values (correlation coefficient:-0.956, p = 0.000). The lateral wall apposition pressure of a 4- or 5-mm aperture size was higher than a 6-mm aperture size for SL-type WEB devices with a 7-mm diameter. Similarly, the lateral wall apposition pressure detected for a 3- or 3.5-mm aperture size was higher than a 4-mm aperture size for W5-4.5-3 and W5-5-3.6. It was observed that maximum lateral wall pressure was detected in plate measurements of SLS-type devices compared to SL-type devices. The diameter and height values of 3 of the 4 unconstrained WEB devices analyzed differed from the catalog values. CONCLUSION: It seems that SLS-type devices apply more pressure on the aneurysm's lateral borders than SL-type devices.

Fracture Detection in Wrist X-ray Images Using Deep Learning-Based Object Detection Models.

Hospitals, especially their emergency services, receive a high number of wrist fracture cases. For correct diagnosis and proper treatment of these, images obtained from various medical equipment must be viewed by physicians, along with the patient's medical records and physical examination. The aim of this study is to perform fracture detection by use of deep-learning on wrist X-ray images to support physicians in the diagnosis of these fractures, particularly in the emergency services. Using SABL, RegNet, RetinaNet, PAA, Libra R-CNN, FSAF, Faster R-CNN, Dynamic R-CNN and DCN deep-learning-based object detection models with various backbones, 20 different fracture detection procedures were performed on Gazi University Hospital's dataset of wrist X-ray images. To further improve these procedures, five different ensemble models were developed and then used to reform an ensemble model to develop a unique detection model, 'wrist fracture detection-combo (WFD-C)'. From 26 different models for fracture detection, the highest detection result obtained was 0.8639 average precision (AP50) in the WFD-C model. Huawei Turkey R&D Center supports this study within the scope of the ongoing cooperation project coded 071813 between Gazi University, Huawei and Medskor.

A Computer-Aided Diagnosis System for Measuring Carotid Artery Intima-Media Thickness (IMT) Using Quaternion Vectors.

This study aims investigating adjustable distant fuzzy c-means segmentation on carotid Doppler images, as well as quaternion-based convolution filters and saliency mapping procedures. We developed imaging software that will simplify the measurement of carotid artery intima-media thickness (IMT) on saliency mapping images. Additionally, specialists evaluated the present images and compared them with saliency mapping images. In the present research, we conducted imaging studies of 25 carotid Doppler images obtained by the Department of Cardiology at Firat University. After implementing fuzzy c-means segmentation and quaternion-based convolution on all Doppler images, we obtained a model that can be analyzed easily by the doctors using a bottom-up saliency model. These methods were applied to 25 carotid Doppler images and then interpreted by specialists. In the present study, we used color-filtering methods to obtain carotid color images. Saliency mapping was performed on the obtained images, and the carotid artery IMT was detected and interpreted on the obtained images from both methods and the raw images are shown in Results. Also these results were investigated by using Mean Square Error (MSE) for the raw IMT images and the method which gives the best performance is the Quaternion Based Saliency Mapping (QBSM). 0,0014 and 0,000191 mm(2) MSEs were obtained for artery lumen diameters and plaque diameters in carotid arteries respectively. We found that computer-based image processing methods used on carotid Doppler could aid doctors' in their decision-making process. We developed software that could ease the process of measuring carotid IMT for cardiologists and help them to evaluate their findings.

The rate of mucosal healing by azathioprine therapy and prediction by artificial systems.

BACKGROUND/AIMS: We aimed to assess the effect of azathioprine on mucosal healing in patients with inflammatory bowel diseases (IBD). Artificial neural networks were applied to IBD data for predicting mucosal remission. MATERIALS AND METHODS: Two thousand seven hundred patients with IBD were evaluated. According to the computer-based study, data of 129 patients with IBD were used. Artificial neural networks were performed and tested. RESULTS: Endoscopic mucosal healing was found in 37% patients with IBD. Male gender group showed a negative impact on the efficacy of azathioprine (p<0.05). Responder patients with IBD were older than the nonresponder (p<0.05) patients. According to this study, the cascade-forward neural network study provides 79.1% correct results. In addition to a 0.16033 training error, mean square error (MSE) was taken at the 16th epoch from the feed-forward back-propagation neural network. This neural structure, used for predicting mucosal remission with azathioprine, was also validated. CONCLUSION: Analyzing all parameters within each other to azathioprine therapy were shown that which parameters gave better healing were determined by statistical, and for the most weighted six input parameters, artificial neural network structures were constructed. In this study, feed-forward back-propagation and cascade-forward artificial neural network models were used.

Analyses of a cirrhotic patient's evolution using self organizing mapping and Child-Pugh scoring.

Due to the importance of cirrhosis evolution, this study examined cirrhotic patients using Self Organizing Mapping (SOM) based on the Child-Pugh scoring method. Because Colored Doppler Ultrasound (CDU) has too many parameters, scoring can be a very difficult task. Classifying cirrhotic patients via SOM and investigating weights of the cirrhotic CDU parameters are aimed in this study. SOM was used to map high dimensional cirrhotic data onto two dimensional clustered data. These clusters provided a feature map of cirrhotic patients. In this study, 103 cirrhotic patients and a control group of 44 healthy individuals were examined in the hospital, and parameters were analyzed using SOM. These data were obtained using CDU, and age and sex parameters were analyzed in this study. Cirrhotic patients were histopathologically separated into subgroups using the Child-Pugh scoring method, and the presence of ascites was determined using SOM. In this study, differences between the control group and cirrhotic patients with their subgroups were investigated using SOM, and the results were discussed. Renal artery indices, hepatic artery indices, portal vein parameters, age and the degree of ascites were analyzed using SOM for a total of 147 individuals. The combination of SOM and Child-Pugh scoring method can be useful for the interpretation of cirrhotic patient's evolution. Computer-based SOM algorithm and negative effectiveness of a large scale dataset could be minimized by adjusting the weight of the parameters. This study will faciliate doctors to make better decisions for their patients.