Imaging-based deep learning has shown great potential in the diagnosis and management of kidney diseases, including both neoplastic and non-neoplastic conditions. This review summarizes recent clinical applications, challenges, and future prospects of imaging-based deep learning in kidney diseases. Deep learning models, particularly convolutional neural networks (CNNs), have been applied to tasks such as kidney tumor segmentation, differential diagnosis, surgical planning, and prognosis prediction. These models can improve the accuracy and efficiency of diagnosis and treatment. However, challenges such as data imbalance, heterogeneity, and the need for large datasets remain. Additionally, the interpretability of algorithms, ethical risks, and bias assessment are important considerations for future development. The review highlights the potential of imaging-based deep learning in clinical practice, including its ability to detect and predict non-neoplastic kidney diseases like chronic kidney disease (CKD) and renal calculi. Despite these challenges, the future of imaging-based deep learning in kidney diseases is promising, with potential for personalized medicine and improved patient outcomes. The review also discusses the importance of data preprocessing, model training, and evaluation in the development of effective deep learning models for kidney diseases. Overall, imaging-based deep learning is a promising tool for improving the diagnosis and management of kidney diseases.Imaging-based deep learning has shown great potential in the diagnosis and management of kidney diseases, including both neoplastic and non-neoplastic conditions. This review summarizes recent clinical applications, challenges, and future prospects of imaging-based deep learning in kidney diseases. Deep learning models, particularly convolutional neural networks (CNNs), have been applied to tasks such as kidney tumor segmentation, differential diagnosis, surgical planning, and prognosis prediction. These models can improve the accuracy and efficiency of diagnosis and treatment. However, challenges such as data imbalance, heterogeneity, and the need for large datasets remain. Additionally, the interpretability of algorithms, ethical risks, and bias assessment are important considerations for future development. The review highlights the potential of imaging-based deep learning in clinical practice, including its ability to detect and predict non-neoplastic kidney diseases like chronic kidney disease (CKD) and renal calculi. Despite these challenges, the future of imaging-based deep learning in kidney diseases is promising, with potential for personalized medicine and improved patient outcomes. The review also discusses the importance of data preprocessing, model training, and evaluation in the development of effective deep learning models for kidney diseases. Overall, imaging-based deep learning is a promising tool for improving the diagnosis and management of kidney diseases.