AIMC Topic: Urinary Calculi

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Noninvasive CT radiomics-clinical model accurately classifies anhydrous uric acid stones: a multicenter study.

World journal of urology
BACKGROUND: Urolithiasis, particularly anhydrous uric acid stones (AUAs), imposes significant clinical and economic burdens. Accurate preoperative differentiation of AUAs from other stone types remains challenging, yet essential for personalized pati...

Automatic detection of urinary stones from non-contrast enhanced computed tomography images.

Urolithiasis
Urinary stones, one of the most common emergency conditions, traverse the ureter, urine flow is obstructed, resulting in hydronephrosis and severe pain. However, vessel wall calcifications or phleboliths are frequently observed in abdominal and pelvi...

Comprehensive analysis of 55,213 stones: understanding common morphological associations advances endoscopic stone recognition and AI integration.

World journal of urology
PURPOSE: To assess the prevalence and associations of urinary stone morphologies, focusing on their relevance for Endoscopic Stone Recognition and improving AI-assisted ESR (AESR) systems.

Construction and validation of a urinary stone composition prediction model based on machine learning.

Urolithiasis
The composition of urinary calculi serves as a critical determinant for personalized surgical strategies; however, such compositional data are often unavailable preoperatively. This study aims to develop a machine learning-based preoperative predicti...

Can Artificial Intelligence Accurately Detect Urinary Stones? A Systematic Review.

Journal of endourology
To perform a systematic review on artificial intelligence (AI) performances to detect urinary stones. A PROSPERO-registered (CRD473152) systematic search of Scopus, Web of Science, Embase, and PubMed databases was performed to identify original res...

Predictive Modeling of Urinary Stone Composition Using Machine Learning and Clinical Data: Implications for Treatment Strategies and Pathophysiological Insights.

Journal of endourology
Preventative strategies and surgical treatments for urolithiasis depend on stone composition. However, stone composition is often unknown until the stone is passed or surgically managed. Given that stone composition likely reflects the physiological...

Detection of urinary tract stones on submillisievert abdominopelvic CT imaging with deep-learning image reconstruction algorithm (DLIR).

Abdominal radiology (New York)
PURPOSE: Urolithiasis is a chronic condition that leads to repeated CT scans throughout the patient's life. The goal was to assess the diagnostic performance and image quality of submillisievert abdominopelvic computed tomography (CT) using deep lear...

Prediction of the composition of urinary stones using deep learning.

Investigative and clinical urology
PURPOSE: This study aimed to predict the composition of urolithiasis using deep learning from urinary stone images.

Value of deep learning reconstruction at ultra-low-dose CT for evaluation of urolithiasis.

European radiology
OBJECTIVES: To determine the diagnostic accuracy and image quality of ultra-low-dose computed tomography (ULDCT) with deep learning reconstruction (DLR) to evaluate patients with suspected urolithiasis, compared with ULDCT with hybrid iterative recon...

Detection of urinary tract calculi on CT images reconstructed with deep learning algorithms.

Abdominal radiology (New York)
BACKGROUND: Deep learning Computed Tomography (CT) reconstruction (DLR) algorithms promise to improve image quality but the impact on clinical diagnostic performance remains to be demonstrated. We aimed to compare DLR to standard iterative reconstruc...