Back to search

Article

SEapp versus MIMIC score for predicting spontaneous ureteral stone passage

2026-04-01

Abstract excerpt

<title>Abstract</title> <p> <bold>PURPOSE</bold> - A generative artificial intelligence–based clinical decision-support tool (Spontaneous Expulsion app; SEapp) was developed and prospectively validated to estimate spontaneous stone passage (SSP) in patients with ureteral lithiasis. The secondary objective was to compare its predictive performance with the MIMIC score. <bold>METHODS</bold> - SEapp was develope...

Topics

Open a Topic to create a Post that cites this publication.

Identifiers and source

Literature Corpus work
94d9da2e-1a9f-51db-8345-253a6e94ff1b
DOI
10.21203/rs.3.rs-9233651/v1
Open publication

Related research

Semantic proximity does not establish scientific evidence.

Click a neighbor to travelStep 1 · 12 closest
Interactive article relationship graphSelect a related publication card to move it into the centre and load its closest explainable connections. Solid lines are source-backed structured connections. Dashed lines are semantic discovery signals and are not scientific evidence.
SEapp versus MIMIC score for predicting spontaneous ureteral stone passageDOI 10.21203/rs.3.rs-9233651/v1
Factors affecting operative time in ureteroscopic stone removal: A retrospective observational study for prediction model constructionSemantic proximity
Factors affecting operative time in ureteroscopic stone removal: A cross-sectional study for prediction model constructionSemantic proximity
Surgical Management of Urolithiasis in France from 2016 to 2022: Epidemiological Data from the National PMSI DatabaseSemantic proximity
Feasibility and safety of a non-indwelling urinary catheter after flexible ureteroscopy with intelligent control of renal pelvis pressure: A retrospective studySemantic proximity
Comparison of percutaneous nephrolithotomy and flexible ureterorenoscopy in the treatment of single upper ureteral calculi larger than 10 mm:a retrospective studySemantic proximity
AI-driven credibility profiling of real-world patient experiences suggests overlooked kidney stone therapies warrant further investigationSemantic proximity
Artificial neural networks in urolithiasis.Semantic proximity
Impact of COVID-19 on the management and outcomes of ureteric stones in the UK: a multicentre retrospective studySemantic proximity
Artificial Intelligence for Automated Recognition of Hepatocystic Anatomy During Laparoscopic Cholecystectomy: Current Evidence, Clinical Readiness, and Future DirectionsSemantic proximity
Radiologist-AI Collaboration for Ischemia Diagnosis in Small Bowel Obstruction: Multicentric Development and External Validation of a Multimodal Deep Learning ModelSemantic proximity
Convolutional Neural Network-Based Model to Differentiate Distal Ureteral Stones, Vascular Calcification, and Normal CasesSemantic proximity
What Is the Preferred Management of Lower Ureteral Stones? SWL or URS - A Critical Evaluation With an Emphasis on the Changes in Patient’s Quality of LifeSemantic proximity
Select a neighboring publication to make it the new centre.