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Review

Imaging Engineering and Artificial Intelligence in Urinary Stone Disease: Low-Dose Computed Tomography, Spectral Technologies, and Predictive Models

by
Shota Iijima
,
Takanobu Utsumi
*,
Rino Ikeda
,
Naoki Ishitsuka
,
Takahide Noro
,
Yuta Suzuki
,
Yuka Sugizaki
,
Takatoshi Somoto
,
Ryo Oka
,
Takumi Endo
,
Naoto Kamiya
and
Hiroyoshi Suzuki
Department of Urology, Toho University Sakura Medical Center, Sakura 285-8741, Japan
*
Author to whom correspondence should be addressed.
Eng 2026, 7(4), 174; https://doi.org/10.3390/eng7040174
Submission received: 17 January 2026 / Revised: 2 April 2026 / Accepted: 9 April 2026 / Published: 11 April 2026
(This article belongs to the Special Issue Advanced Artificial Intelligence Techniques for Disease Prediction, Diagnosis and Management)
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Abstract

Urinary stone disease is common, recurrent, and increasingly managed through imaging-driven pathways, yet standard-dose CT of the kidneys, ureters, and bladder (CT KUB) raises concerns about cumulative radiation exposure and the limited use of quantitative imaging information for risk stratification. This review synthesizes contemporary evidence on dose-optimized CT, advanced spectral technologies, and artificial intelligence (AI)-enabled analytics that are reshaping diagnosis, treatment selection, and triage. This review summarizes data supporting low-dose and ultra-low-dose CT protocols that preserve diagnostic accuracy while substantially reducing dose, and discusses how dual-energy CT, photon-counting CT, and radiomics facilitate noninvasive stone characterization and extraction of imaging biomarkers beyond size and location. It also reviews AI approaches for automated detection, segmentation, and volumetric quantification across CT, KUB, and ultrasounds, highlighting their potential to standardize stone-burden metrics. It further examines predictive models, including logistic regression, nomograms, and machine learning, for perioperative infectious complications, emergency department admission or intervention, procedure success, and long-term recurrence, and outlines reporting and validation frameworks and implementation considerations, including software as a medical device regulation and human oversight. In contrast to prior reviews that consider imaging and AI separately, this review integrates dose reduction, spectral characterization, and AI-driven analytics within real-world clinical pathways to distinguish established clinical applications from those that remain investigational. Integrating advanced CT and AI outputs into well-validated prediction models embedded in real-world workflows may enable safer imaging, more consistent triage, and more personalized follow-up for urinary stone disease.
Keywords: urolithiasis; low-dose computed tomography; spectral CT; dual-energy CT; photon-counting CT; radiomics; deep learning; risk prediction urolithiasis; low-dose computed tomography; spectral CT; dual-energy CT; photon-counting CT; radiomics; deep learning; risk prediction

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MDPI and ACS Style

Iijima, S.; Utsumi, T.; Ikeda, R.; Ishitsuka, N.; Noro, T.; Suzuki, Y.; Sugizaki, Y.; Somoto, T.; Oka, R.; Endo, T.; et al. Imaging Engineering and Artificial Intelligence in Urinary Stone Disease: Low-Dose Computed Tomography, Spectral Technologies, and Predictive Models. Eng 2026, 7, 174. https://doi.org/10.3390/eng7040174

AMA Style

Iijima S, Utsumi T, Ikeda R, Ishitsuka N, Noro T, Suzuki Y, Sugizaki Y, Somoto T, Oka R, Endo T, et al. Imaging Engineering and Artificial Intelligence in Urinary Stone Disease: Low-Dose Computed Tomography, Spectral Technologies, and Predictive Models. Eng. 2026; 7(4):174. https://doi.org/10.3390/eng7040174

Chicago/Turabian Style

Iijima, Shota, Takanobu Utsumi, Rino Ikeda, Naoki Ishitsuka, Takahide Noro, Yuta Suzuki, Yuka Sugizaki, Takatoshi Somoto, Ryo Oka, Takumi Endo, and et al. 2026. "Imaging Engineering and Artificial Intelligence in Urinary Stone Disease: Low-Dose Computed Tomography, Spectral Technologies, and Predictive Models" Eng 7, no. 4: 174. https://doi.org/10.3390/eng7040174

APA Style

Iijima, S., Utsumi, T., Ikeda, R., Ishitsuka, N., Noro, T., Suzuki, Y., Sugizaki, Y., Somoto, T., Oka, R., Endo, T., Kamiya, N., & Suzuki, H. (2026). Imaging Engineering and Artificial Intelligence in Urinary Stone Disease: Low-Dose Computed Tomography, Spectral Technologies, and Predictive Models. Eng, 7(4), 174. https://doi.org/10.3390/eng7040174

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