Artificial neural networks (ANNs) ability to learn, correct errors, and transform a large amount of raw data into beneficial medical decisions for treatment and care has increased in popularity for enhanced patient safety and quality of care. Therefore, this paper reviews the critical role of ANNs in providing valuable insights for patients’ healthcare decisions and efficient disease diagnosis. We study different types of ANNs in the existing literature that advance ANNs’ adaptation for complex applications. Specifically, we investigate ANNs’ advances for predicting viral, cancer, skin, and COVID-19 diseases. Furthermore, we propose a deep convolutional neural network (CNN) model called ConXNet, based on chest radiography images, to improve the detection accuracy of COVID-19 disease. ConXNet is trained and tested using a chest radiography image dataset obtained from Kaggle, achieving more than 97% accuracy and 98% precision, which is better than other existing state-of-the-art models, such as DeTraC, U-Net, COVID MTNet, and COVID-Net, having 93.1%, 94.10%, 84.76%, and 90% accuracy and 94%, 95%, 85%, and 92% precision, respectively. The results show that the ConXNet model performed significantly well for a relatively large dataset compared with the aforementioned models. Moreover, the ConXNet model reduces the time complexity by using dropout layers and batch normalization techniques. Finally, we highlight future research directions and challenges, such as the complexity of the algorithms, insufficient available data, privacy and security, and integration of biosensing with ANNs. These research directions require considerable attention for improving the scope of ANNs for medical diagnostic and treatment applications.

Neural Networks for the Detection of COVID-19 and Other Diseases: Prospects and Challenges

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 Tools in Pediatric Urology: A Comprehensive Review of Recent Advances

Machine Learning and Artificial Intelligence to Improve Interpretation of Urodynamics

ABSTRACT Aims The integration of artificial intelligence (AI) into functional urology management must be assessed for its clinical utility, but hopefully will change, perhaps to revolutionize the way LUTD and other conditions are assessed, the aim being to offer patients more rapid and effective management which enhances patient outcomes. The aim of this proposal, discussed at the ICI‐RS annual meeting, is to evaluate the available evidence on AI and the way it might change the approach to urodynamic (UDS) diagnoses, including overactive bladder syndrome (OAB), and perhaps other LUTDs such as bladder outflow obstruction. Methods A compendium of discussion based on the current evidence related to AI and its potential applications in UDS and OAB. Results AI‐powered diagnostic tools are being developed to analyze complex datasets from urodynamic studies, imaging, and other diagnostic tests. AI systems can leverage large volumes of clinical data to recommend personalized treatment plans based on individual patient profiles to optimize surgical procedures, enhance diagnostic precision, tailor the therapy, reduce the risk of complications, and improve outcomes. In the future, AI will be able to provide tailored counseling regarding the outcomes and potential side effects of drugs and procedures to a given patient. Conclusion AI's role in functional urology has been poorly investigated, and its implementation across several areas may improve clinical care and the pathophysiological understanding of functional urologic conditions. 

How Can We Show That Artificial Intelligence May Improve Our Assessment and Management of Lower Urinary Tract Dysfunctions?—ICI‐RS 2024

Prenatal Hydronephrosis (PNH) is caused in fetal infants identified by obstruction in the urine production, it leads to the enlargement of the kidney in fetal infants. Hydronephrosis is a common inherited defect that causes dilatation of the renal pelvis and calyces in newborns. The hydronephrosis disease grading is developed by the society for fetal urology (SFU) to get the accurate diagnosis for the decision making. This method involves the diverse population of ultrasound images of patients. Preprocessing methods in the images are used to enhance the visual quality of the image and reduce noise in the ultrasound images. This research study involves the filtering algorithms to segment the ultrasound images and it includes the convolutional neural network for classification. The severity of the hydronephrosis is measured by the extent of renal pelvis and calyceal dilation. The outcome of the study is to predict the prenatal hydronephrosis in the early stage by using the network model, the segmentation of the image shows the predictive capability of the model in distinguishing mild, moderate, and severe cases of hydronephrosis.

ATheoretical Study on Prenatal Hydronephrosis: Image Segmentation Techniques and CNN Model

Automated Society of Fetal Urology (SFU) grading of hydronephrosis on ultrasound imaging using a convolutional neural network.

The ongoing Bacillus Calmette-Guérin (BCG) shortage has created challenges for the treatment of non-muscle invasive bladder cancer (NMIBCa). Our objective was to evaluate the efficacy of reduced-dose induction BCG (RD-iBCG) compared to full-dose induction BCG (FD-iBCG) regarding recurrence rates. We hypothesized that patients receiving RD-iBCG may recur at a higher rate compared to those who received FD-iBCG therapy. A retrospective review of all patients with NMIBCa treated with intravesical therapy at our institution between 2015-2020 was conducted. Inclusion criteria consisted of having a diagnosis of AUA intermediate or high-risk NMIBCa with an indication for a six-week induction course of FD or RD-BCG with at least 1 year of documented follow up. The data were censored at one year. Propensity score matching for age, sex, tumor pathology, and initial vs. recurrent disease was performed. The primary endpoint was bladder cancer recurrence, reported as recurrence-free survival. A total of 254 patients were reviewed for this study. Our final cohort was 139 patients after exclusion. Thirty-nine percent of patients had HGT1 disease. 38.6% of patients receiving RD-BCG developed a recurrence of bladder cancer within a one-year follow-up as compared to 33.7% of patients receiving FD therapy. After propensity matching, this value remained statistically significant (p = 0.03). In conclusion, RD-iBCG for NMIBCa is associated with a significantly greater risk of recurrence than full-dose induction therapy, suggesting that RD-iBCG may not be equivalent or non-inferior to full-dose administration in the short term. 

Diminished Short-Term Efficacy of Reduced-Dose Induction BCG in the Treatment of Non-Muscle Invasive Bladder Cancer

INTRODUCTION
The presence of sarcomatoid features in localized renal cell carcinoma (RCC) is associated with worse outcomes. We sought to use a national database to evaluate the outcomes and prognosis of metastatic RCC (mRCC) with sarcomatoid features treated with cytoreductive nephrectomy (CN) and targeted therapy (TT).


METHODS
The National Cancer Database (2010-2013) was used to identify patients with mRCC at diagnosis. Only patients who underwent CN followed by TT were included. Kaplan-Meier curves, log-rank test, and multivariate Cox regression analysis were used to compare overall survival (OS) between mRCC with and without sarcomatoid features. Subgroup analysis in patients with clear cell RCC (ccRCC) was performed.


RESULTS
A total of 1,427 patients with mRCC treated with CN followed by TT were included of which 364 (26%) had mRCC with sarcomatoid features. mRCC with sarcomatoid features were more likely to have Fuhrman grade 4 cancer. mRCC with sarcomatoid features had worse OS than mRCC without sarcomatoid features (24.6 vs 12.0 months, P < 0.001). For the clear cell cohort, mRCC with sarcomatoid features had worse OS than mRCC without sarcomatoid features (26.2 vs 14.0 months, P < 0.001). Multivariate Cox regression showed sarcomatoid features was significantly associated with worse OS in the overall cohort (hazard ratio [HR] =1.63, 95% confidence interval [CI] =1.38-1.91, P < 0.001) and the ccRCC subcohort (HR=1.53, 95% CI=1.23-1.90, P < 0.001).


DISCUSSION/CONCLUSION
mRCC with sarcomatoid features treated with CN and TT has a very poor and drastically different prognosis compared with mRCC without sarcomatoid features. With the expansion of systemic RCC therapies, investigation is needed to optimize treatment in this high-risk cohort.

Prognostic significance of sarcomatoid features in metastatic renal cell carcinoma treated with cytoreductive nephrectomy and targeted therapy.

Mp41-18 clinical manifestations of birt-hogg-dube syndrome in a large clinical cohort

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Automated Society of Fetal Urology (SFU) grading of hydronephrosis on ultrasound imaging using a convolutional neural network.

Diminished Short-Term Efficacy of Reduced-Dose Induction BCG in the Treatment of Non-Muscle Invasive Bladder Cancer

Prognostic significance of sarcomatoid features in metastatic renal cell carcinoma treated with cytoreductive nephrectomy and targeted therapy.

Mp41-18 clinical manifestations of birt-hogg-dube syndrome in a large clinical cohort