Renal cyst evolution in childhood: a contemporary observational study

Approach to simple kidney cysts in children

The finding of a simple kidney cyst in a child can pose a diagnostic and management challenge for pediatric nephrologists, urologists, and primary care providers. The reported prevalence varies from 0.22 to 1% in large ultrasonography-based series of more than 10,000 children each. The true prevalence, however, may be higher or lower, as factors such as variations in referral patterns, indications for ultrasonography, or technical considerations could impact prevalence rates. For many patients, simple kidney cysts may be found incidentally when imaging is performed for another indication. Although simple cysts can occur in children, they may also represent the first sign of autosomal dominant polycystic kidney disease (ADPKD) or other less common cystic kidney diseases. Definitive guidelines regarding the evaluation and monitoring of children with simple kidney cysts have not been established. The desire on the part of the practitioner and/or parents to establish a definitive diagnosis should be balanced with the cost and inconvenience of repeated imaging and visits with specialists. The goals of this review are to (1) outline the definition, epidemiology, clinical presentation, and natural history of simple kidney cysts in childhood; (2) describe clinical features that could suggest a diagnosis other than a simple kidney cyst; and (3) present a suggested framework for evaluating and monitoring of children with one or more simple kidney cysts.

A rare case of parapelvic cyst: A case report

Parapelvic cyst (PPC) is a rare cyst that arises from the renal parenchyma and usually does not cause symptoms. However, PPC may cause compression of the pelvicalyceal system resulting in obstructive symptoms. Ultrasonography may not be sufficient to diagnose PPC since they are often misdiagnosed with hydronephrosis, thus computed tomography of the renal is required. In this case, we present, a 27 years old male with a 3 years history of partial urinary retention presented with intermittent loin pain. Hydronephrosis was found on ultrasonography but failed to identify the source of obstruction. A CT scan was conducted and a PPC was confirmed. Nephrostomy was scheduled for ureteral dilation and the patient responded well 4 months after surgery. In this case, we highlight the role of radiology in identifying and differentiating the PPC.

Cystic kidney diseases in children

Cystic kidney disease comprises a broad group of heterogeneous diseases, which differ greatly in age at onset, disease manifestation, systemic involvement, disease progression, and long-term prognosis. As our understanding of these diseases continues to evolve and new treatment strategies continue to emerge, correctly differentiating and diagnosing these diseases becomes increasingly important. In this review, we aim to highlight the key features of the most relevant cystic kidney diseases, underscore important diagnostic characteristics of each disease, and present specific management options if applicable.

Kidney Diseases Classification using Hybrid Transfer-Learning DenseNet201-Based and Random Forest Classifier

There are several disease kinds in global populations that may be related to human lifestyles, social, genetic, economic, and other factors related to the nature of the country they live in. Most of the recent studies have focused on investigating prevalent diseases that spread in the population in order to minimize mortality risks, choose the best method for treatment, and improve community healthcare. Kidney disease is one of the most widespread health problems in modern society. This study focuses on kidney stones, cysts, and tumors, the three most common types of renal illness, using a dataset of 12,446 CT urogram and whole abdomen images, aiming to move toward an AI-based kidney disease diagnosis system while contributing to the wider field of artificial intelligence research. In this study, a hybrid technique is used by utilizing both pre-train models for feature extraction and classification using machine learning algorithms for the task of kidney disease image diagnosis. The pre-trained model used in this study is the Densenet-201 model. As well as using Random Forest for classification, the Densenet-201-Random-Forest approach has outperformed many of the previous models used in other studies, having an accuracy rate of 99.719 percent.

Vision transformer and explainable transfer learning models for auto detection of kidney cyst, stone and tumor from CT-radiography

Renal failure, a public health concern, and the scarcity of nephrologists around the globe have necessitated the development of an AI-based system to auto-diagnose kidney diseases. This research deals with the three major renal diseases categories: kidney stones, cysts, and tumors, and gathered and annotated a total of 12,446 CT whole abdomen and urogram images in order to construct an AI-based kidney diseases diagnostic system and contribute to the AI community's research scope e.g., modeling digital-twin of renal functions. The collected images were exposed to exploratory data analysis, which revealed that the images from all of the classes had the same type of mean color distribution. Furthermore, six machine learning models were built, three of which are based on the state-of-the-art variants of the Vision transformers EANet, CCT, and Swin transformers, while the other three are based on well-known deep learning models Resnet, VGG16, and Inception v3, which were adjusted in the last layers. While the VGG16 and CCT models performed admirably, the swin transformer outperformed all of them in terms of accuracy, with an accuracy of 99.30 percent. The F1 score and precision and recall comparison reveal that the Swin transformer outperforms all other models and that it is the quickest to train. The study also revealed the blackbox of the VGG16, Resnet50, and Inception models, demonstrating that VGG16 is superior than Resnet50 and Inceptionv3 in terms of monitoring the necessary anatomy abnormalities. We believe that the superior accuracy of our Swin transformer-based model and the VGG16-based model can both be useful in diagnosing kidney tumors, cysts, and stones.