ArticleInternational braz j urol : official journal of the Brazilian Society of Urology
Deep learning model-assisted detection of kidney stones on computed tomography.
Article in International braz j urol : official journal of the Brazilian Society of Urology. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers, 3 of them syntheses that pooled it.
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Who cites it
26 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- A holistic guide to effective prevention and treatment for kidney stones: a systematic review exploring anti-urolithiasis approaches.Naunyn-Schmiedeberg's archives of pharmacology · 2026Pooled it
- Artificial intelligence in urolithiasis: a systematic review of utilization and effectiveness.World journal of urology · 2024Pooled it
- Retrograde intrarenal surgery with or without ureteral access sheath: a systematic review and meta-analysis of randomized controlled trials.International braz j urol : official journal of the Brazilian Society of UrologyPooled it
- Design and validation of renal stone detection using multi-architecture feature extraction with deep sequential learning model on axial computed tomography images.Scientific reports · 2026Article
- [Development of a Computer-Aided Automatic-Detection Deep-Learning Algorithm to Identify a Urinary Stone in Low-Dose Non-Enhanced CT Images].Journal of the Korean Society of Radiology · 2026Article
- Article
- The Role of Artificial Intelligence in Modern Medicine: Clinical Applications, Economic Implications, and Ethical Considerations.Current pharmaceutical design · 2026Review
- Review
- Fine-tuned deep learning models for early detection and classification of kidney conditions in CT imaging.Scientific reports · 2025Article
- UrologiQ: AI-based accurate detection, measurement and reporting of stones in CT-KUB scans.Urolithiasis · 2024Review
- Predicting symptomatic kidney stones using machine learning algorithms: insights from the Fasa adults cohort study (FACS).BMC research notes · 2024Article
- Automatic Urinary Stone Detection System for Abdominal Non-Enhanced CT Images Reduces the Burden on Radiologists.Journal of imaging informatics in medicine · 2024Article
- Identification of kidney stones in KUB X-ray images using VGG16 empowered with explainable artificial intelligence.Scientific reports · 2024Article
- Acoustic emission of kidney stones: a medical adaptation of statistical breakdown mechanisms.Urolithiasis · 2024Article
- Imaging-based deep learning in kidney diseases: recent progress and future prospects.Insights into imaging · 2024Review
- Article
- [Imaging for urolithiasis].Urologie (Heidelberg, Germany) · 2023Review
- Dose Optimization Using a Deep Learning Tool in Various CT Protocols for Urolithiasis: A Physical Human Phantom Study.Medicina (Kaunas, Lithuania) · 2023Article
- Transforming urinary stone disease management by artificial intelligence-based methods: A comprehensive review.Asian journal of urology · 2023Review
- Deep learning techniques for imaging diagnosis of renal cell carcinoma: current and emerging trends.Frontiers in oncology · 2023Review
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5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
introductionThe aim of this study was to investigate the success of a deep learning model in detecting kidney stones in different planes according to stone size on unenhanced computed tomography (CT) images. MATERIALS AND
methodsThis retrospective study included 455 patients who underwent CT scanning for kidney stones between January 2016 and January 2020; of them, 405 were diagnosed with kidney stones and 50 were not. Patients with renal stones of 0-1 cm, 1-2 cm, and >2 cm in size were classified into groups 1, 2, and 3, respectively. Two radiologists reviewed 2,959 CT images of 455 patients in three planes. Subsequently, these CT images were evaluated using a deep learning model. The accuracy rate, sensitivity, specificity, and positive and negative predictive values of the deep learning model were determined.
resultsThe training group accuracy rates of the deep learning model were 98.2%, 99.1%, and 97.3% in the axial plane; 99.1%, 98.2%, and 97.3% in the coronal plane; and 98.2%, 98.2%, and 98.2% in the sagittal plane, respectively. The testing group accuracy rates of the deep learning model were 78%, 68% and 70% in the axial plane; 63%, 72%, and 64% in the coronal plane; and 85%, 89%, and 93% in the sagittal plane, respectively.
conclusionsThe use of deep learning algorithms for the detection of kidney stones is reliable and effective. Additionally, these algorithms can reduce the reporting time and cost of CT-dependent urolithiasis detection, leading to early diagnosis and management.
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