Few-Shot Learning for Medical Image Segmentation Using 3D U-Net and Model-Agnostic Meta-Learning (MAML).

Deep learning has attained state-of-the-art results in general image segmentation problems; however, it requires a substantial number of annotated images to achieve the desired outcomes. In the medical field, the availability of annotated images is often limited. To address this challenge, few-shot learning techniques have been successfully adapted to rapidly generalize to new tasks with only a few samples, leveraging prior knowledge. In this paper, we employ a gradient-based method known as Model-Agnostic Meta-Learning (MAML) for medical image segmentation. MAML is a meta-learning algorithm that quickly adapts to new tasks by updating a model's parameters based on a limited set of training samples. Additionally, we use an enhanced 3D U-Net as the foundational network for our models. The enhanced 3D U-Net is a convolutional neural network specifically designed for medical image segmentation. We evaluate our approach on the TotalSegmentator dataset, considering a few annotated images for four tasks: liver, spleen, right kidney, and left kidney. The results demonstrate that our approach facilitates rapid adaptation to new tasks using only a few annotated images. In 10-shot settings, our approach achieved mean dice coefficients of 93.70%, 85.98%, 81.20%, and 89.58% for liver, spleen, right kidney, and left kidney segmentation, respectively. In five-shot sittings, the approach attained mean Dice coefficients of 90.27%, 83.89%, 77.53%, and 87.01% for liver, spleen, right kidney, and left kidney segmentation, respectively. Finally, we assess the effectiveness of our proposed approach on a dataset collected from a local hospital. Employing five-shot sittings, we achieve mean Dice coefficients of 90.62%, 79.86%, 79.87%, and 78.21% for liver, spleen, right kidney, and left kidney segmentation, respectively.

AI-driven deep CNN approach for multi-label pathology classification using chest X-Rays.

Artificial intelligence (AI) has played a significant role in image analysis and feature extraction, applied to detect and diagnose a wide range of chest-related diseases. Although several researchers have used current state-of-the-art approaches and have produced impressive chest-related clinical outcomes, specific techniques may not contribute many advantages if one type of disease is detected without the rest being identified. Those who tried to identify multiple chest-related diseases were ineffective due to insufficient data and the available data not being balanced. This research provides a significant contribution to the healthcare industry and the research community by proposing a synthetic data augmentation in three deep Convolutional Neural Networks (CNNs) architectures for the detection of 14 chest-related diseases. The employed models are DenseNet121, InceptionResNetV2, and ResNet152V2; after training and validation, an average ROC-AUC score of 0.80 was obtained competitive as compared to the previous models that were trained for multi-class classification to detect anomalies in x-ray images. This research illustrates how the proposed model practices state-of-the-art deep neural networks to classify 14 chest-related diseases with better accuracy.

Prevalence of hypothyroidism among chronic kidney disease patients in security force hospital (SFH) in Saudi Arabia.

INTRODUCTION: Impairment in kidney function leads to disturbed thyroid physiology. All levels of the hypothalamic-pituitary-thyroid axis may be involved, including alterations in hormone production, distribution, and excretion, and even CKD progress with hypothyroidism. AIM OF WORK: To assess the prevalence of hypothyroidism among chronic kidney disease patients. MATERIALS AND METHODS: A cross-sectional analysis was conducted in the nephrology department of security forces hospital from January 2015 to February 2018. Biochemical tests (includes blood urea, serum creatinine, PTH, total T4, TSH) were carried out to all participants. RESULTS: Out of 255 CKD patients in the present study, 166 patients had no hypothyroidism, 43 had subclinical hypothyroidism, and 46 had hypothyroidism. The percentage of hypothyroidism among CKD patients was 34.9%, including dialysis patients and 17.66% after exclusion. Out of 24 peritoneal dialysis patients in the current study (P = 0.03), 7 had subclinical hypothyroidism and another 7 had hypothyroidism. In addition, out of 139 hemodialysis patients (P = 0.02), 20 patients had subclinical hypothyroidism and 18 had hypothyroidism. The majority (67.36%) of CKD patients were in CKD stage 5 and had no hypothyroidism (45.10%). Only 29 (11.37%) patients in CKD stage 5 had hypothyroidism and 28 (10.89%) patients had subclinical hypothyroidism. T4 was higher in nondialysis patients, whereas TSH and PTH were higher in dialysis patients. CONCLUSION: The prevalence of hypothyroidism among chronic kidney disease patients was high and increased with the decrease in estimated GFR.