# Deep learning in renal ultrasound: applications, challenges, and future outlook

## TL;DR

This paper reviews how deep learning can improve kidney ultrasound by making it more accurate and automated, though challenges remain.

## Contribution

The paper systematically summarizes deep learning applications in renal ultrasound tasks and highlights future directions for clinical integration.

## Key findings

- Deep learning improves accuracy and efficiency in kidney disease analysis, including chronic kidney disease.
- Challenges remain in data quality, model interpretability, and generalization for clinical use.
- Future directions include combining deep learning with multimodal data and interpretable AI.

## Abstract

Kidney disease poses a significant global health burden, often progressing to end-stage renal disease with serious complications. Renal ultrasound, which is real-time, accessible, and noninvasive, serves as a primary imaging tool for evaluating renal structure and pathology. However, its diagnostic accuracy is limited by interobserver variability. Artificial intelligence (AI), particularly deep learning (DL), offers a promising solution for enhancing objectivity and automation throughout the renal ultrasound workflow. This review systematically summarizes DL applications across key tasks—including kidney segmentation, volume measurement, functional prediction, and disease diagnosis—and evaluates the performance of models such as CNNs and transformers. The results indicate that DL has significantly improved the accuracy and efficiency of kidney disease analysis, including chronic kidney disease (CKD), but challenges remain in terms of data quality, model interpretability, generalizations, and clinical integration. In the future, the combination of DL with multimodal data, large model technology, federated learning and interpretable artificial intelligence will be essential to achieve intelligence, standardization and personalization of renal ultrasound.