ArticleMedical physics2022
A deep learning system for automated kidney stone detection and volumetric segmentation on noncontrast CT scans.
Article in Medical physics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 37 papers.
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37 citing papers in PubMed, 116 citations in OpenAlex.
- Validation of an Automated CT Image Analysis in the Prevention of Urinary Stones with Hydration Trial.Journal of endourology · 2025Trial
- Perioperative Fast-Track Surgery Nursing Intervention for Patients with Kidney Stone Disease under Computed Tomography Imaging.Contrast media & molecular imaging · 2023Trial
- Accuracy and precision of automated kidney stone detection on CT.Abdominal radiology (New York) · 2026Article
- Automated Structured Pre-Report Generation for Nephrolithiasis on Non-Contrast CT: Development and Report-Level Evaluation of a Deep Learning Framework.Journal of imaging informatics in medicine · 2026Article
- Prospective pilot evaluation of a deep learning model for kidney stone detection on CT using a web-based workflow platform.International urology and nephrology · 2026Article
- From conventional imaging software to artificial intelligence: tools for stone volume assessment in urolithiasis. Α review by the EAU and YAU sections of endourology.World journal of urology · 2026Review
- 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
- Bridging the gap in PCNL preoperative planning: software-free stone metrics that outperform conventional measures.BMC urology · 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
- NephroNet: a calibration-aware, patient-disjoint benchmark for multiclass kidney CT classification with a compact depthwise-separable CNN.Frontiers in medicine · 2026Article
- Urological diagnostics based on kidney stone detection in CT imaging using YOLOv8 deep learning framework.Frontiers in medicine · 2026Article
- Assessing Change in Stone Burden on Baseline and Follow-Up CT: Radiologist and Radiomics Evaluations.Journal of imaging · 2025Article
- Automatic detection of urinary stones from non-contrast enhanced computed tomography images.Urolithiasis · 2025Article
- Stone volume instead of maximum stone diameter: results from an international survey.BJU international · 2025Article
- Fine-tuned deep learning models for early detection and classification of kidney conditions in CT imaging.Scientific reports · 2025Article
- Enhanced breast cancer diagnosis using modified InceptionNet-V3: a deep learning approach for ultrasound image classification.Frontiers in physiology · 2025Article
- Comprehensive analysis and validation of TP73 as a biomarker for calcium oxalate nephrolithiasis using machine learning and in vivo and in vitro experiments.Urolithiasis · 2024Article
- Automatic kidney stone identification: an adaptive feature-weighted LSTM model based on urine and blood routine analysis.Urolithiasis · 2024Article
- Kidney medicine meets computer vision: a bibliometric analysis.International urology and nephrology · 2024Article
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4 authors at 2 institutions in 1 country.
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Abstract
purposeEarly detection and size quantification of renal calculi are important for optimizing treatment and preventing severe kidney stone disease. Prior work has shown that volumetric measurements of kidney stones are more informative and reproducible than linear measurements. Deep learning-based systems that use abdominal noncontrast computed tomography (CT) scans may assist in detection and reduce workload by removing the need for manual stone volume measurement. Prior to this work, no such system had been developed for use on noisy low-dose CT or tested on a large-scale external dataset.
methodsWe used a dataset of 91 CT colonography (CTC) scans with manually marked kidney stones combined with 89 CTC scans without kidney stones. To compare with a prior work half the data was used for training and half for testing. A set of CTC scans from 6185 patients from a separate institution with patient-level labels were used as an external validation set. A 3D U-Net model was employed to segment the kidneys, followed by gradient-based anisotropic denoising, thresholding, and region growing. A 13 layer convolutional neural network classifier was then applied to distinguish kidney stones from false positive regions.
resultsThe system achieved a sensitivity of 0.86 at 0.5 false positives per scan on a challenging test set of low-dose CT with many small stones, an improvement over an earlier work that obtained a sensitivity of 0.52. The stone volume measurements correlated well with manual measurements (
conclusionsOur deep-learning-based system showed improvements over a previously developed system that did not use deep learning, with even higher performance on an external validation set.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.