Aim. To evaluate the diagnostic value of ultrasonography and magnetic resonance imaging (MRI) in patients with ulnar neuropathy at the elbow (UNE). Methods. We prospectively performed electrodiagnostic, ultrasonographic, and MRI studies in UNE patients and healthy controls. Three cross-sectional area (CSA) measurements of the ulnar nerve at multiple levels along the arm and maximum CSA(-max) were recorded. Results. The ulnar nerve CSA measurements were different between the UNE severity grades (P < 0.05). CSA-max had the greatest sensitivity (93%) and specificity (68%). Moreover, CSA-max ≥10 mm2 defined the severe UNE cases (sensitivity/specificity: 82%/72%). In MRI, ulnar nerve hyperintensity had the greatest sensitivity (90%) and specificity (80%). Conclusion. Ultrasonography using CSA-max is sensitive and specific in UNE diagnosis and discriminating the severe UNE cases. Furthermore, MRI particularly targeting at increased signal of the ulnar nerve can be a useful diagnostic test of UNE.

Background: Doppler flow velocity waveform analysis of fetal vessels is one of the main methods for evaluating fetus health before labor. Doppler waves of middle cerebral artery (MCA) can predict most of the at risk fetuses in high risk pregnancies. In this study, we tried to obtain normal values and their nomograms during pregnancy for Doppler flow velocity indices of MCA in 20 -40 weeks of normal pregnancies in Iranian population and compare their pattern with other countries' nomograms.

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U-Net based algorithms, due to their complex computations, include limitations when they are used in clinical devices. In this paper, we addressed this problem through a novel U-Net based architecture that called fast and accurate U-Net for medical image segmentation task. The proposed fast and accurate U-Net model contains four tuned 2D-convolutional, 2D-transposed convolutional, and batch normalization layers as its main layers. There are four blocks in the encoder-decoder path. The results of our proposed architecture were evaluated using a prepared dataset for head circumference and abdominal circumference segmentation tasks, and a public dataset (HC18-Grand challenge dataset) for fetal head circumference measurement. The proposed fast network significantly improved the processing time in comparison with U-Net, dilated U-Net, R2U-Net, attention U-Net, and MFP U-Net. It took 0.47 seconds for segmenting a fetal abdominal image. In addition, over the prepared dataset using the proposed accurate model, Dice and Jaccard coefficients were 97.62% and 95.43% for fetal head segmentation, 95.07%, and 91.99% for fetal abdominal segmentation. Moreover, we have obtained the Dice and Jaccard coefficients of 97.45% and 95.00% using the public HC18-Grand challenge dataset. Based on the obtained results, we have concluded that a fine-tuned and a simple well-structured model used in clinical devices can outperform complex models.