Ignite Creation Date:
2024-07-17 @ 11:32 AM
Last Modification Date:
2024-10-26 @ 3:33 PM
Study NCT ID:
NCT06486649
Status:
RECRUITING
Last Update Posted:
2024-07-03
First Post:
2023-12-22
Brief Title:
Application of Multimodal Large Language Model in HFpEF
Sponsor:
Peking University Third Hospital
Organization:
Peking University Third Hospital
Study Overview
Official Title:
Application of a Multimodal Large Language Model to Assist Diagnosis for Heart Failure With Preserved Ejection Fraction
Status:
RECRUITING
Status Verified Date:
2024-06
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:
MeG-HFpEF
Brief Summary:
This study will validate the effectiveness of a multimodal large language model to screen for heart failure with preserved ejection fraction HFpEF comparing it with the traditional clinical standardized assessment process
Detailed Description:
Heart failure is a major complication of various heart diseases and is the leading lethal cause of cardiovascular death worldwide Based on the left ventricular ejection fraction LVEF heart failure can be divided into heart failure with reduced ejection fraction HFrEF heart failure with preserved ejection fraction HFpEF and heart failure with mildly reduced ejection fraction HFmrEF Heart failure rehospitalization rates and in-hospital complications did not differ between HFrEF and HFpEF However over the past two decades the survival rate of HFrEF has improved significantly whereas HFpEF has remained stagnant One of the major reasons for this is that the diagnostic process of HFpEF is complicated and it is easy to cause missed diagnosis in the clinic resulting in delayed treatment
Multimodal large language models are capable of integrating and analyzing medical data from different sources including textual data eg medical records medical literature image data eg electrocardiograms CT scan images and audio data eg symptoms narrated by patients This multimodal data integration capability is crucial for understanding complex medical scenarios as it provides a more comprehensive view of the condition than a single data source
The diagnosis of HFpEF faces many challenges and requires clinicians to make judgments on multi-dimensional data which can easily lead to the underdiagnosis and misdiagnosis of the disease As a generative artificial intelligence tool a large language model is able to integrate and analyze data from different sources and has the ability to learn and evolve from existing clinical evidence Based on this this study intends to evaluate the effectiveness of multimodal large language model for screening for heart failure with preserved ejection fraction HFpEF comparing it with the traditional clinical standard assessment process
Multimodal large language models are capable of integrating and analyzing medical data from different sources including textual data eg medical records medical literature image data eg electrocardiograms CT scan images and audio data eg symptoms narrated by patients This multimodal data integration capability is crucial for understanding complex medical scenarios as it provides a more comprehensive view of the condition than a single data source
The diagnosis of HFpEF faces many challenges and requires clinicians to make judgments on multi-dimensional data which can easily lead to the underdiagnosis and misdiagnosis of the disease As a generative artificial intelligence tool a large language model is able to integrate and analyze data from different sources and has the ability to learn and evolve from existing clinical evidence Based on this this study intends to evaluate the effectiveness of multimodal large language model for screening for heart failure with preserved ejection fraction HFpEF comparing it with the traditional clinical standard assessment process
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