Prediction of Spontaneous Ureteral Calculous Passage by an Artificial Neural Network

Abstract

An artificial neural network may be used to predict accurately the probability of spontaneous ureteral stone passage. Using such a model at presentation may help to determine whether a patient should receive early intervention for a stone or expect a lengthy interval before stone passage.

The role of Silodosin as a new medical expulsive therapy for ureteral stones: a meta-analysis

“…31 The definitive management of stones in these patients is usually deferred because approximately 68%-98% of stones <5 mm are expected to pass spontaneously. 32,33 As stone size increases, however, spontaneous passage becomes less likely; 34,35 the estimated spontaneous passage rate is 47% for stones >5 mm and <10 mm. 36 There were 60.3% stone-free rate (SFR) in distal ureteral stone and 39.3% SFR in upper ureteral stone, respectively.…”

Randomized Prospective Trial Comparing Immediate Versus Delayed Ureteroscopy for Patients with Ureteral Calculi and Normal Renal Function Who Present to the Emergency Department

“…17 Moreover, even the simple watchful waiting approach can result in complications, such as urinary tract infection or hydronephrosis, and can even affect renal function. Cummings and colleagues 18 reported identification of the pretreatment duration of symptoms in ureteral stones as being the most important factor for prediction of treatment outcome and spontaneous stone passage in an artificial network.…”

The Role for Active Monitoring in Urinary Stones: A Systematic Review

“…These have been created with historic retrospective data or case series. 10,19 Most studies agree that duration of symptoms before seeking medical attention and degree of hydronephrosis may have some influence on outcome.…”