Kidney stone disease, or nephrolithiasis, represents a significant global health concern, often leading to recurrent episodes, urinary obstruction, and chronic renal complications. Timely and accurate detection was critical for effective management, yet conventional imaging modalities, including computed tomography (CT) and ultrasound, are limited by operator dependency, inter-observer variability, and interpretational delays. Recent advancements in artificial intelligence (AI) offer transformative potential in automating the detection, characterization, and prediction of kidney stones from medical imaging data. This chapter presents a comprehensive examination of AI-enabled approaches, focusing on machine learning and deep learning techniques that enhance diagnostic accuracy, reduce clinical workload, and provide predictive insights into stone growth, recurrence, and treatment outcomes. Multimodal strategies integrating CT and ultrasound images are explored to improve sensitivity, specificity, and robustness in real-world clinical scenarios. The chapter further addresses data acquisition, preprocessing, augmentation, synthetic image generation, feature extraction, hyperparameter optimization, and model evaluation methodologies. Emphasis was placed on error analysis, robustness assessment, and clinical validation to ensure the reliability and interpretability of AI predictions. Future directions, including federated learning, explainable AI, and longitudinal predictive modeling, are discussed to support precision medicine in urology. By integrating AI into hospital workflows, these approaches aim to advance early detection, personalized treatment planning, and optimized patient care in kidney stone management.
Kidney stone disease, clinically known as nephrolithiasis, represents a significant urological disorder with increasing prevalence worldwide [1]. It affects millions of individuals annually and poses substantial healthcare challenges due to its recurrent nature and potential for severe renal complications [2]. The formation of stones within the renal system was influenced by multiple factors, including dietary habits, metabolic imbalances, genetic predisposition, dehydration, and lifestyle-related conditions [3]. Clinically, kidney stones can result in pain, hematuria, urinary obstruction, and chronic kidney damage if left undiagnosed or untreated. Conventional imaging techniques, primarily computed tomography (CT) and ultrasound, play a central role in detecting and characterizing stones [4]. CT imaging provides high-resolution, cross-sectional visualization, enabling precise assessment of stone size, shape, density, and anatomical location. Ultrasound offers a non-invasive and cost-effective alternative that was particularly useful for populations where radiation exposure must be minimized, such as children and pregnant women. Despite their diagnostic capabilities, both modalities rely heavily on manual interpretation, which was time-consuming and prone to inter-observer variability, leading to inconsistencies in detection rates and delays in initiating treatment [5].
The emergence of artificial intelligence (AI) in healthcare has provided a transformative pathway to enhance diagnostic accuracy, efficiency, and clinical decision-making [6]. AI, particularly deep learning architectures such as convolutional neural networks (CNNs), enables automated analysis of complex medical imaging data by extracting hierarchical and subtle features that may be overlooked by human interpretation [7]. These algorithms are capable of identifying kidney stones, quantifying their characteristics, and even differentiating between stone types based on imaging features. In addition to detection, AI models can predict clinically relevant outcomes such as stone growth, potential recurrence, and treatment response, thus offering a predictive dimension beyond traditional imaging analysis [8]. The ability of AI systems to process large volumes of CT and ultrasound images with high consistency reduces the dependency on operator expertise, minimizes human error, and accelerates the diagnostic workflow [9]. Multimodal AI approaches that integrate both CT and ultrasound data can further improve sensitivity, specificity, and robustness, capturing complementary anatomical and textural information [10].
