Fully automated kidney image biomarker prediction in ultrasound scans using Fast-Unet+.

Any kidney dimension and volume variation can be a remarkable indicator of kidney disorders. Precise kidney segmentation in standard planes plays an undeniable role in predicting kidney size and volume. On the other hand, ultrasound is the modality of choice in diagnostic procedures. This paper proposes a convolutional neural network with nested layers, namely Fast-Unet++, promoting the Fast and accurate Unet model. First, the model was trained and evaluated for segmenting sagittal and axial images of the kidney. Then, the predicted masks were used to estimate the kidney image biomarkers, including its volume and dimensions (length, width, thickness, and parenchymal thickness). Finally, the proposed model was tested on a publicly available dataset with various shapes and compared with the related networks. Moreover, the network was evaluated using a set of patients who had undergone ultrasound and computed tomography. The dice metric, Jaccard coefficient, and mean absolute distance were used to evaluate the segmentation step. 0.97, 0.94, and 3.23 mm for the sagittal frame, and 0.95, 0.9, and 3.87 mm for the axial frame were achieved. The kidney dimensions and volume were evaluated using accuracy, the area under the curve, sensitivity, specificity, precision, and F1.

Influence of HLA-A, -B, -DR Polymorphisms on the Severity of COVID-19: A Case-Control Study in the Iranian Population.

BACKGROUND: As an emerging pandemic disease, COVID-19 encompasses a spectrum of clinical diagnoses, from the common cold to severe respiratory syndrome. Considering the shreds of evidence demonstrating the relationship between human leukocyte antigen (HLA) allele diversity and infectious disease susceptibility, this study was conducted to determine the association of HLA alleles with COVID-19 severity in Iranian subjects. METHODS: In this case-control study, a total of 200 unrelated individuals (consisting of 100 people with severe COVID-19 and an average age of 55.54 as the case group, and 100 patients with mild COVID-19 with an average age of 48.97 as the control group) were recruited, and HLA typing (Locus A, B, and DR) was performed using the Olerup sequence-specific oligonucleotide (SSO) HLA-typing kit. RESULTS: Our results showed that HLA-A*11 and HLA-DRB1*14 alleles were more frequently observed in severe COVID-19 cases, while HLA-B*52 was more common in mild cases, which was in agreement with some previous studies. CONCLUSION: Our results confirmed the evidence for the association of HLA alleles with COVID-19 outcomes. We found that HLA-A*11 and HLA-DRB1*14 alleles may be susceptibility factors for severe COVID-19, while HLA-B*52 may be a protective factor. These findings provide new insight into the pathogenesis of COVID-19 and help patient management.

Clinical target segmentation using a novel deep neural network: double attention Res-U-Net.

We introduced Double Attention Res-U-Net architecture to address medical image segmentation problem in different medical imaging system. Accurate medical image segmentation suffers from some challenges including, difficulty of different interest object modeling, presence of noise, and signal dropout throughout the measurement. The base line image segmentation approaches are not sufficient for complex target segmentation throughout the various medical image types. To overcome the issues, a novel U-Net-based model proposed that consists of two consecutive networks with five and four encoding and decoding levels respectively. In each of networks, there are four residual blocks between the encoder-decoder path and skip connections that help the networks to tackle the vanishing gradient problem, followed by the multi-scale attention gates to generate richer contextual information. To evaluate our architecture, we investigated three distinct data-sets, (i.e., CVC-ClinicDB dataset, Multi-site MRI dataset, and a collected ultrasound dataset). The proposed algorithm achieved Dice and Jaccard coefficients of 95.79%, 91.62%, respectively for CRL, and 93.84% and 89.08% for fetal foot segmentation. Moreover, the proposed model outperformed the state-of-the-art U-Net based model on the external CVC-ClinicDB, and multi-site MRI datasets with Dice and Jaccard coefficients of 83%, 75.31% for CVC-ClinicDB, and 92.07% and 87.14% for multi-site MRI dataset, respectively.