ArticleEuropean radiology2020
Dose independent characterization of renal stones by means of dual energy computed tomography and machine learning: an ex-vivo study.
Article in European radiology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers, 2 of them syntheses that pooled it.
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Who cites it
17 citing papers in PubMed, 2 syntheses or guidelines pooled it, 42 citations in OpenAlex.
- Artificial intelligence in urolithiasis: a systematic review of utilization and effectiveness.World journal of urology · 2024Pooled it
- The Ascent of Artificial Intelligence in Endourology: a Systematic Review Over the Last 2 Decades.Current urology reports · 2021Pooled it
- What you don't see matters: dual-energy CT in emergency radiology.Japanese journal of radiology · 2026Review
- The high-risk factors of urinary infectious stones: a retrospective study.Translational andrology and urology · 2025Article
- Prediction of the composition of urinary calculi using artificial intelligence.Pakistan journal of medical sciences · 2025Article
- Radiomics-driven spectral profiling of six kidney stone types with monoenergetic CT reconstructions in photon-counting CT.European radiology · 2025Article
- Kidney Stones as Minerals: How Methods from Geology Could Inform Urolithiasis Treatment.Journal of clinical medicine · 2025Review
- Use of Artificial Intelligence for Analyzing Kidney Stone Composition: Are We There Yet?Mayo Clinic proceedings. Digital health · 2023Article
- Dual-energy CT kidney stone characterization-can diagnostic accuracy be achieved at low radiation dose?European radiology · 2023Article
- Transforming urinary stone disease management by artificial intelligence-based methods: A comprehensive review.Asian journal of urology · 2023Review
- Theranostic roles of machine learning in clinical management of kidney stone disease.Computational and structural biotechnology journal · 2023Review
- Machine Learning for Renal Pathologies: An Updated Survey.Sensors (Basel, Switzerland) · 2022Review
- [A nonlocal spectral similarity-induced material decomposition method for noise reduction of dual-energy CT images].Nan fang yi ke da xue xue bao = Journal of Southern Medical University · 2022Article
- Artificial Intelligence Applications in Urology: Reporting Standards to Achieve Fluency for Urologists.The Urologic clinics of North America · 2022Review
- Machine Learning Prediction of Kidney Stone Composition Using Electronic Health Record-Derived Features.Journal of endourology · 2022Article
- Value of artificial intelligence model based on unenhanced computed tomography of urinary tract for preoperative prediction of calcium oxalate monohydrate stonesAnnals of translational medicine · 2021Article
- Protocol analysis of dual-energy CT for optimization of kidney stone detection in virtual non-contrast reconstructions.European radiology · 2020Article
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Authors and funding
7 authors at 1 institution in 1 country.
Funding
Abstract
objectivesTo predict the main component of pure and mixed kidney stones using dual-energy computed tomography and machine learning.
methods200 kidney stones with a known composition as determined by infrared spectroscopy were examined using a non-anthropomorphic phantom on a spectral detector computed tomography scanner. Stones were of either pure (monocrystalline, n = 116) or compound (dicrystalline, n = 84) composition. Image acquisition was repeated twice using both, normal and low-dose protocols, respectively (ND/LD). Conventional images and low and high keV virtual monoenergetic images were reconstructed. Stones were semi-automatically segmented. A shallow neural network was trained using data from ND1 acquisition split into training (70%), testing (15%) and validation-datasets (15%). Performance for ND2 and both LD acquisitions was tested. Accuracy on a per-voxel and a per-stone basis was calculated.
resultsMain components were: Whewellite (n = 80), weddellite (n = 21), Ca-phosphate (n = 39), cysteine (n = 20), struvite (n = 13), uric acid (n = 18) and xanthine stones (n = 9). Stone size ranged from 3 to 18 mm. Overall accuracy for predicting the main component on a per-voxel basis attained by ND testing dataset was 91.1%. On independently tested acquisitions, accuracy was 87.1-90.4%.
conclusionsEven in compound stones, the main component can be reliably determined using dual energy CT and machine learning, irrespective of dose protocol. KEY POINTS: • Spectral Detector Dual Energy CT and Machine Learning allow for an accurate prediction of stone composition. • Ex-vivo study demonstrates the dose independent assessment of pure and compound stones. • Lowest accuracy is reported for compound stones with struvite as main component.
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