Ignite Creation Date:
2025-12-24 @ 7:48 PM
Ignite Modification Date:
2025-12-29 @ 9:06 AM
Study NCT ID:
NCT06330103
Status:
COMPLETED
Last Update Posted:
2024-03-26
First Post:
2024-03-05
Is NOT Gene Therapy:
True
Has Adverse Events:
False
Brief Title:
Efficacy of AI EF Screening by Using Smartphone Application Recorded PLAX View Cardiac Ultrasound Video Clips
Sponsor:
Rayong Hospital
Organization:
Study Overview
Official Title:
Assessing the Efficacy of Artificial Intelligence in Left Ventricular Function Screening Using Parasternal Long Axis View Cardiac Ultrasound Video Clips
Status:
COMPLETED
Status Verified Date:
2024-03
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:
Assessing the Efficacy of Artificial Intelligence in Left Ventricular Function Screening Using Parasternal Long Axis View Cardiac Ultrasound Video Clips
ABSTRACT BACKGROUND: Echocardiography serves as a fundamental diagnostic procedure for managing heart failure patients. Data from Thailand's Ministry of Public Health reveals that there is a substantial patient population, with over 100,000 admissions annually due to this condition. Nevertheless, the widespread implementation of echocardiography in this patient group remains challenging, primarily due to limitations in specialist resources, particularly in rural community hospitals. Although modern community hospitals are equipped with ultrasound machines capable of basic cardiac assessment (e.g., parasternal long axis view), the demand for expert cardiologists remains a formidable obstacle to achieving comprehensive diagnostic capabilities. Leveraging the capabilities of Artificial Intelligence (AI) technology, proficient in the accurate prediction and processing of diverse healthcare data types, offers a promising for addressing this prevailing issue. This study is designed to assess the effectiveness of AI in evaluating cardiac performance from parasternal long axis view ultrasound video clips obtained via the smartphone application.
OBJECTIVES: To evaluate the effectiveness of artificial intelligence in screening cardiac function from parasternal long axis view cardiac ultrasound video clips obtained through the smartphone application.
ABSTRACT BACKGROUND: Echocardiography serves as a fundamental diagnostic procedure for managing heart failure patients. Data from Thailand's Ministry of Public Health reveals that there is a substantial patient population, with over 100,000 admissions annually due to this condition. Nevertheless, the widespread implementation of echocardiography in this patient group remains challenging, primarily due to limitations in specialist resources, particularly in rural community hospitals. Although modern community hospitals are equipped with ultrasound machines capable of basic cardiac assessment (e.g., parasternal long axis view), the demand for expert cardiologists remains a formidable obstacle to achieving comprehensive diagnostic capabilities. Leveraging the capabilities of Artificial Intelligence (AI) technology, proficient in the accurate prediction and processing of diverse healthcare data types, offers a promising for addressing this prevailing issue. This study is designed to assess the effectiveness of AI in evaluating cardiac performance from parasternal long axis view ultrasound video clips obtained via the smartphone application.
OBJECTIVES: To evaluate the effectiveness of artificial intelligence in screening cardiac function from parasternal long axis view cardiac ultrasound video clips obtained through the smartphone application.
Detailed Description:
METHODS: The investigators built the smartphone application to collect parasternal long axis view video clips and used artificial intelligence "Easy EF" to evaluate cardiac function. All samples that were evaluated for LVEF by certified cardiologists, 70% of all clips were used to train AI, while the remaining 30% of clips were used to test if AI could process the results correctly. Artificial intelligence aims to classify cardiac function into three groups: Reduced EF, Mildly Reduced EF, and Preserved LV.
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?: