Ignite Creation Date:
2024-05-06 @ 8:23 PM
Last Modification Date:
2024-10-26 @ 3:27 PM
Study NCT ID:
NCT06369909
Status:
RECRUITING
Last Update Posted:
2024-04-17
First Post:
2024-04-06
Brief Title:
Study on AI-assisted Multimodal Diagnosis System of Autoimmune Pancreatitis
Sponsor:
Peking Union Medical College Hospital
Organization:
Peking Union Medical College Hospital
Study Overview
Official Title:
Study on AI-assisted Multimodal Diagnosis System of Autoimmune Pancreatitis Based on the Mechanism of Tfh Activation by Intestinal Bacteria and Endosonographic Features
Status:
RECRUITING
Status Verified Date:
2024-04
Last Known Status:
None
Delayed Posting:
No
If Stopped, Why?:
Not Stopped
Has Expanded Access:
False
If Expanded Access, NCT#:
N/A
Has Expanded Access, NCT# Status:
N/A
Acronym:
None
Brief Summary:
The existing comprehensive diagnostic system for autoimmune pancreatitis AIP is complex with multidimensional clinical information including morphological changes and a lack of specific biomarkers Endoscopic ultrasound EUS can provide all the elements for morphological diagnosis of AIP but the long learning curve and large observer differences make it difficult to popularize and promote The cooperation units of the three regions in this project have found in the early stage that Klebsiella pneumoniae KP induced follicular helper T cells Tfh activation is an important mechanism of AIP but the identification of pathogenic components of the strain and clinical validation need to be explored We have established a national multicenter AIP queue in the early stage and extracted EUS audio-visual features to establish a scoring model but intelligent assistance is still needed to improve efficiency Therefore we plan to integrate gut microbiota Tfh activation markers and EUS imaging features to establish an AI assisted multimodal diagnostic system for AIP This study will collaborate across multiple centers to identify and validate the components that induce Tfh activation in KP bacterial cells to extract EUS pancreatic ultrasound features and optimize artificial intelligence assisted diagnostic algorithms and to establish and validate an artificial intelligence assisted multimodal diagnostic system based on clinical information biomarkers and EUS The aim of this study is to provide new diagnosis and treatment evaluation methods for AIP with high accuracy convenience and easy promotion for clinical practice
Detailed Description:
None
Study Oversight
Has Oversight DMC:
None
Is a FDA Regulated Drug?:
False
Is a FDA Regulated Device?:
False
Is an Unapproved Device?:
None
Is a PPSD?:
None
Is a US Export?:
None
Is an FDA AA801 Violation?:
None