Kidney stones are one of the most common renal disorders that can produce severe complications if not diagnosed and treated early. Recently, advances in AI have ensured that deep learning and explainable AI enable the automatic segmentation and detection of kidney stones from medical imaging, thus improving diagnostic efficiency and accuracy. For this review, eighteen representative studies using machine learning, deep learning, and hybrid models for kidney stone segmentation were considered, which were published in the period between 2020 and 2025. The XAI techniques being mainly utilized with the discussed models in the study are SHAP, LIME, Grad-CAM, Layer-wise Relevance Propagation, and EigenCAM. Such approaches tend to enhance clinicians’ trust in allowing early diagnosis and supporting clinical decision-making, especially in resource-constrained settings. Regardless of the towering results, this area still suffers due to certain limitations such as lack of diversity in datasets, absence of multimodal integration, and scarcity of real-world validation. All in all, integrating DL with XAI presents a transparent, reliable, and clinically acceptable approach to detecting and segmenting kidney stones.
Hossen et al. (Tue,) studied this question.