Abdus Salam

Background: Technetium-99m dimercaptosuccinic acid (DMSA) scintigraphy plays a critical role in pediatric imaging for detecting renal cortical scarring, which is essential for diagnosing and managing kidney damage in children. However, variability in observer interpretation poses challenges, potentially impacting clinical decision-making and outcomes. Objective: This study aims to assess intra- and inter-observer agreement in interpreting DMSA scans for detecting renal cortical scarring in pediatric patients, focusing on the presence, location, and percentage of kidney involvement. Materials and methods: This prospective study analyzed 220 pediatric patients with suspected renal scarring. Four experienced radiologists independently reviewed DMSA scans on two separate occasions, 3–4 weeks apart, using standardized assessment criteria. Intra-observer agreement was measured using Cohen’s kappa, while inter-observer agreement was assessed using pairwise Cohen’s kappa for categorical evaluations and Kendall’s tau-b for the percentage of kidney involvement. Results: Strong intra-observer agreement was observed across all four radiologists, with Cohen’s kappa values for renal scarring stages ranging from 0.704 to 0.955. Observer-4 consistently showed the highest agreement across all metrics. Inter-observer agreement varied substantially depending on observer pairs. Pairs excluding Observer-2 demonstrated moderate to substantial agreement (kappa up to 0.8268 and Kendall’s tau-b up to 0.7192), while pairs involving Observer-2 showed poor to slight agreement. Variability was particularly notable in assessing scarring severity and defect localization. Conclusion: While intra-observer consistency in interpreting DMSA scans is high, inter-observer variability remains a concern, especially in evaluating the severity and location of renal scarring. These findings underscore the need for standardized protocols and targeted training to enhance diagnostic accuracy. Moreover, the development of validated datasets could support the advancement of machine learning models for automated, precise detection of renal scarring, ultimately improving diagnostic reliability and patient outcomes. Graphical

Urinary Tract Infections (UTIs) are common medical diseases that occur from bacterial infections which can impact any area of the urinary system. Detecting urine particles is crucial for diagnosing UTIs and other renal abnormalities, as the presence of red blood cells (RBCs), white blood cells (WBCs), casts, fungi, and bacteria in urine can indicate disorders such as hematuria, kidney stones, and urinary tract malignancies. This study introduces an advanced deep learning method that utilizes ensemble of YOLOv9e and KD-YOLOX-ViT models. YOLOv9e model is built upon the Generalized Efficient Layer Aggregation Network (GELAN) and Programmable Gradient Information (PGI). The KD-YOLOX-ViT model integrates knowledge distillation and Vision Transformer (ViT) modules within the YOLOX framework to improve performance. This ensemble framework combines the strengths of YOLOv9e and KD-YOLOX-ViT models to automate the detection and classification of seven distinct urine sediment particles, effectively addressing challenges related to class imbalance, image resolution, and domain adaptation. The YOLOv9e model exhibited exceptional performance, obtaining precision, recall, and mean average precision (mAP50) scores of 88.5%, 88.1%, and 92.2%, respectively. The KD-YOLOX-ViT model also performed well, achieving precision, recall, mAP50, and mAP50-95 scores of 86%, 88%, 86.7%, and 53.3%, respectively. By ensembling YOLOv9e and KD-YOLOX-ViT using Weighted Box Fusion (WBF), the model achieved a final mAP50 of 94.18%. The model’s adaptability was further validated through rigorous external validation on a novel dataset, yielding in a mAP50 of 94.64%. Comparative analysis with state-of-the-art methods confirms the model’s real-time analytical capabilities. Moreover, the integration of eXplainable AI (XAI) enhances interpretability, offering valuable insights for confident clinical diagnosis. This comprehensive approach shows significant promise in advancing diagnostic accuracy and enabling earlier, real-time treatment on a global scale.

Artificial intelligence (AI) is providing novel answers to long-standing clinical problems, and it is quickly changing pediatric urology. This thorough analysis focuses on current developments in AI technologies that improve pediatric urology diagnosis, treatment planning, and surgery results. Deep learning algorithms help detect problems with previously unheard-of precision in disorders including hydronephrosis, pyeloplasty, and vesicoureteral reflux, where AI-powered prediction models have demonstrated promising outcomes in boosting diagnostic accuracy. AI-enhanced image processing methods have significantly improved the quality and interpretation of medical images. Examples of these methods are deep-learning-based segmentation and contrast limited adaptive histogram equalization (CLAHE). These methods guarantee higher precision in the identification and classification of pediatric urological disorders, and AI-driven ground truth construction approaches aid in the standardization of and improvement in training data, resulting in more resilient and consistent segmentation models. AI is being used for surgical support as well. AI-assisted navigation devices help with difficult operations like pyeloplasty by decreasing complications and increasing surgical accuracy. AI also helps with long-term patient monitoring, predictive analytics, and customized treatment strategies, all of which improve results for younger patients. However, there are practical, ethical, and legal issues with AI integration in pediatric urology that need to be carefully navigated. To close knowledge gaps, more investigation is required, especially in the areas of AI-driven surgical methods and standardized ground truth datasets for pediatric radiologic image segmentation. In the end, AI has the potential to completely transform pediatric urology by enhancing patient care, increasing the effectiveness of treatments, and spurring more advancements in this exciting area.

Artificial intelligence (AI) is transforming many healthcare fields including pediatric urology. This in-depth review explores recent advances in AI tools for pediatric urology, emphasizing how state-of-the-art technologies can be used to enhance patient treatment. An initial overview of pediatric urology is given to provide context for this interdisciplinary field and the wide spectrum of problems this entails, before discussing potential uses of AI to improve diagnostic accuracy, treatment planning, and surgical results. AI-powered predictive models are already changing pediatric clinical practice by guiding interpretation of medical images and supporting decision making. In addition to outlining the aims, methods, and conclusions of previous studies on AI applications in pediatric urology, this review also highlights knowledge gaps and priority areas for future study. Particular focus is given to specialized surgeries such pyeloplasty, where AI-guided treatment may help overcome specific technical challenges of this complex procedure. Also highlighted are practical, ethical, and legal aspects of integrating AI into pediatric urology, which will be crucial for responsible innovation in patient-centered treatment. Overall, this article aims to enlighten and inspire clinicians, researchers, and other healthcare stakeholders by offering insight into the future of AI in pediatric urology.

Mulberry leaves serve as the primary food source for Bombyx mori silkworms, crucial for silk thread production. However, mulberry trees are highly susceptible to diseases, spreading rapidly and causing significant losses. Manual disease identification across large farms is arduous and time-consuming. Leveraging computer vision for early disease detection and classification can mitigate up to 90% of production losses. This study collected leaves from two regions of Bangladesh, categorized as healthy, leaf rust-affected, and leaf spot-affected. With a total of 1091 images, split into training (764), testing (218), and validation (109) sets for 5-fold cross-validation, preprocessing and augmentation yielded 6,000 images, including synthetics. Comparing three pretrained convolutional neural networks (CNNs) - MobileNetV3_Small, ResNet50, and VGG19 - augmented with four additional layers, the modified MobileNetV3_Small excelled in precision, recall, F1-score, and accuracy, achieving notable results of 97.0%, 96.4%, 96.4%, and 96.4%, respectively, across cross-validation folds. An efficient smartphone application employing the proposed model for mulberry leaf disease recognition was developed. Overall, the model outperformed existing State of the Art (SOTA) approaches, showcasing its effectiveness in disease identification.

Biosciences, Biotechnology Research Asia is an international, open access, peer reviewed research journal covering all aspects of Biosciences and Biotechnology field.