Artificial Intelligence in Inflammatory Bowel Disease Endoscopy
Sabrina Gloria Giulia Testoni; Guglielmo Albertini Petroni; Maria Laura Annunziata; Giuseppe Dell’Anna; Michele Puricelli; Claudia Delogu; Vito Annese · 2025 · Diagnostics
WASTE classifies this as Negative / Null Result Report · AI classification, approximate
The study found no significant effect — useful as a negative control or null benchmark for your own design.
Abstract
Inflammatory bowel diseases (IBDs), comprising Crohn's disease (CD) and ulcerative colitis (UC), are chronic immune-mediated inflammatory diseases of the gastrointestinal (GI) tract with still-elusive etiopathogeneses and an increasing prevalence worldwide. Despite the growing availability of more advanced therapies in the last two decades, there are still a number of unmet needs. For example, the achievement of mucosal healing has been widely demonstrated as a prognostic marker for better outcomes and a reduced risk of dysplasia and cancer; however, the accuracy of endoscopy is crucial for bo
Abstract by Sabrina Gloria Giulia Testoni; Guglielmo Albertini Petroni; Maria Laura Annunziata; Giuseppe Dell’Anna; Michele Puricelli; Claudia Delogu; Vito Annese, Diagnostics (2025) — licensed CC BY 4.0.
About to run something similar?
Run an AI Precheck on your own design to catch failure modes like this one before you spend the time. Your first desk check is free.
Related failures
A Randomized Trial of Intraarterial Treatment for Acute Ischemic Stroke
Negative / Null Result ReportDuodenal Infusion of Donor Feces for Recurrent Clostridium difficile
Negative / Null Result ReportStenting versus Endarterectomy for Treatment of Carotid-Artery Stenosis
Negative / Null Result ReportEffects of Combination Lipid Therapy in Type 2 Diabetes Mellitus
Replication FailureA Randomized Trial of Bevacizumab for Newly Diagnosed Glioblastoma
Negative / Null Result ReportSpironolactone for Heart Failure with Preserved Ejection Fraction
WASTE indexes this work — it does not host or republish it. Failure-type classification is automated and approximate.
Metadata source: OpenAlex · DOI 10.3390/diagnostics15070905
