ClinicalTrials.gov
public full text
Public Domain
Development and Validation of a Deep Learning Algorithm to Evaluate Endoscopic Disease Activity of Ulcerative Colitis.
The purpose of this study is to develop an artificial intelligence(AI) assisted scoring system, which can evaluate the disease severity and mucosal healing stage in patients with ulcerative colitis. Then testify whether this new scoring system can help physicians to enhance the accuracy of disease severity assessments in a multi-center clinical practice.
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- Recent Advances in Artificial Intelligence for Endoscopic and Multimodal Assessment of Inflammatory Bowel Disease: A Review.#1054353
- Artificial Intelligence in Gallbladder Imaging: A Rapid Evidence Review and Exploratory Meta-Analysis of Diagnostic Performance and Reader Assistance.#785745
- Predicting vesicoureteral reflux outcomes using artificial intelligence: A critical appraisal using APPRAISE-AI#1133715
- Diagnosis and Characterization of Non-Alcoholic Fatty Liver Disease Based on Artificial Intelligence.#1031394
- Artificial intelligence in inflammatory bowel disease: From current evidence, clinical translation, and the road to precision medicine.#388774
Record · ID 1093402
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive