Purpose: Kidney stone prevention relies on the 24-hour urine collection to diagnose
metabolic abnormalities and direct dietary and pharmacological therapy. While its use is …

Objectives: To assess the accuracy of machine learning models in predicting kidney stone
composition using variables extracted from the electronic health record (EHR). Materials and …

Introduction Urolithiasis is a complex medical entity and regroups several different types of
stones, each caused by a multitude of dietary imbalances or metabolic anomalies. In order …

Purpose: The decision-making of how to treat urinary infection stones was complicated by
the difficulty in preoperative diagnosis of these stones. Hence, we developed machine …

Purpose: Preventative strategies and surgical treatments for urolithiasis depend on stone
composition. However, stone composition is often unknown until the stone is passed or …

Objectives To examine the differences in 24‐h urine composition between nephrolithiasis
patients with and without diabetes mellitus (DM) in a large cohort of stone‐formers and to …

Abstract Introduction Dual energy CT (DECT) is a recent technique that is increasingly being
used to differentiate between calcium and uric acid urinary tract calculi. The aim of this study …

Objectives: Urinary biochemistry is used to detect and monitor conditions associated with
recurrent kidney stones. There are no predictive machine learning (ML) tools for kidney …

Abstract Background and Purpose: Distinguishing calcium oxalate from uric acid stones is
critical to identify those patients who may benefit from dissolution therapy and can also help …

The correct diagnosis of uric acid (UA) stones has important clinical implications since
patients with a high risk of perioperative morbidity may be spared surgical intervention and …