Renal Stone Picture and Values for the Identification of Chronic Renal Illness

This study seeks to respond to the growing incidence of kidney stones by creating an AI-based solution for accurate detection and complete functional assessment. Through the use of Convolutional Neural Networks (CNNs), specifically the VGG16 model, the system scans CT images to identify renal stones with high accuracy. In addition to stone detection, it assesses key renal parameters like urinary tract obstruction and tissue perfusion. The methodology involves data curation, preprocessing, and model training, ensuring robustness across diverse patient cases. Performance metrics such as sensitivity, specificity, and precision validate the model's reliability. This research contributes to nephrology by integrating deep learning with medical imaging, enabling early diagnosis and improved patient outcomes. The system incorporates explainable AI techniques to provide insights into the decision-making process, aiding clinical interpretability. Real-world validation is conducted using a diverse dataset, ensuring generalizability across different demographics. This approach aims to support radiologists by offering a rapid, automated tool for comprehensive kidney function analysis.