The article reviews the recent progress and future prospects of imaging-based deep learning in kidney diseases. Deep learning, particularly convolutional neural networks (CNNs), has shown significant potential in improving the accuracy and efficiency of diagnosing and managing kidney diseases, including both neoplastic and non-neoplastic conditions. The review covers the methodology of deep learning, its clinical applications, and the challenges and future directions in this field. **Methodology of Deep Learning:** - **Data Acquisition:** Large, diverse datasets are crucial for training deep learning models. Multi-source data can reduce bias but may also increase the difficulty of convergence. - **Data Preprocessing:** Techniques such as noise reduction, image normalization, and registration are essential. Data augmentation, including generative adversarial networks (GANs), helps reduce overfitting. - **Model Selection and Training:** CNNs are commonly used due to their ability to automatically learn discriminative features. Model architecture selection, training, and hyperparameter adjustment are critical steps. - **Evaluation:** Metrics like accuracy, precision, F1-score, AUROC, Dice similarity coefficient, and Jaccard similarity coefficient are used to evaluate model performance. **Clinical Applications:** - **Renal Tumor Segmentation:** Deep learning models have improved the accuracy of tumor segmentation, especially for large tumors. Techniques like 3D Multi-Scale Residual Fully Convolutional Neural Networks have shown promising results. - **Differential Diagnosis:** Deep learning models can accurately differentiate between renal cell carcinoma (RCC) and benign renal masses, improving diagnostic accuracy. - **Tumor Staging and Grading:** Models can grade RCC, providing valuable information for treatment planning and monitoring. - **Surgical Planning:** Fine segmentation of renal arteries and vascular structures is crucial for preoperative planning, with deep learning models showing good performance. - **Prognosis Prediction:** Deep learning models can predict the prognosis of RCC patients, contributing to better clinical management. - **Non-Neoplastic Renal Diseases:** Deep learning has been applied to detect chronic kidney disease (CKD), evaluate kidney function, and detect renal calculi, showing potential for early intervention and improved patient outcomes. **Challenges and Future Prospects:** - **Data Imbalance and Heterogeneity:** Balancing data and addressing heterogeneity remain significant challenges. - **Interpretability and Ethical Risks:** Understanding how deep learning models make predictions is crucial for clinical trust and ethical considerations. - **Future Directions:** Research is needed to improve interpretability, develop normative reporting guidelines, and address ethical risks. Topological data analysis (TDA) and ensemble learning are potential solutions. The article concludes that while deep learning in kidney diseases shows great promise, further research is needed to overcome current challenges and fully realize its potential in clinical practice.The article reviews the recent progress and future prospects of imaging-based deep learning in kidney diseases. Deep learning, particularly convolutional neural networks (CNNs), has shown significant potential in improving the accuracy and efficiency of diagnosing and managing kidney diseases, including both neoplastic and non-neoplastic conditions. The review covers the methodology of deep learning, its clinical applications, and the challenges and future directions in this field. **Methodology of Deep Learning:** - **Data Acquisition:** Large, diverse datasets are crucial for training deep learning models. Multi-source data can reduce bias but may also increase the difficulty of convergence. - **Data Preprocessing:** Techniques such as noise reduction, image normalization, and registration are essential. Data augmentation, including generative adversarial networks (GANs), helps reduce overfitting. - **Model Selection and Training:** CNNs are commonly used due to their ability to automatically learn discriminative features. Model architecture selection, training, and hyperparameter adjustment are critical steps. - **Evaluation:** Metrics like accuracy, precision, F1-score, AUROC, Dice similarity coefficient, and Jaccard similarity coefficient are used to evaluate model performance. **Clinical Applications:** - **Renal Tumor Segmentation:** Deep learning models have improved the accuracy of tumor segmentation, especially for large tumors. Techniques like 3D Multi-Scale Residual Fully Convolutional Neural Networks have shown promising results. - **Differential Diagnosis:** Deep learning models can accurately differentiate between renal cell carcinoma (RCC) and benign renal masses, improving diagnostic accuracy. - **Tumor Staging and Grading:** Models can grade RCC, providing valuable information for treatment planning and monitoring. - **Surgical Planning:** Fine segmentation of renal arteries and vascular structures is crucial for preoperative planning, with deep learning models showing good performance. - **Prognosis Prediction:** Deep learning models can predict the prognosis of RCC patients, contributing to better clinical management. - **Non-Neoplastic Renal Diseases:** Deep learning has been applied to detect chronic kidney disease (CKD), evaluate kidney function, and detect renal calculi, showing potential for early intervention and improved patient outcomes. **Challenges and Future Prospects:** - **Data Imbalance and Heterogeneity:** Balancing data and addressing heterogeneity remain significant challenges. - **Interpretability and Ethical Risks:** Understanding how deep learning models make predictions is crucial for clinical trust and ethical considerations. - **Future Directions:** Research is needed to improve interpretability, develop normative reporting guidelines, and address ethical risks. Topological data analysis (TDA) and ensemble learning are potential solutions. The article concludes that while deep learning in kidney diseases shows great promise, further research is needed to overcome current challenges and fully realize its potential in clinical practice.