Efficacy of AI EF Screening by Using Smartphone Application Recorded PLAX View Cardiac Ultrasound Video Clips

NCT ID: NCT06330103

Last Updated: 2024-03-26

Basic Information

• Recruitment Status: COMPLETED
• Clinical Phase: NA
• Total Enrollment: 923 participants
• Study Classification: INTERVENTIONAL
• Study Start Date: 2023-05-01
• Study Completion Date: 2023-07-31

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.

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.

Conditions

Heart Failure; Heart Failure With Reduced Ejection Fraction; Cardiac Failure; Echocardiography; Artificial Intelligence

Study Design

• Allocation Method: NON_RANDOMIZED
• Intervention Model: PARALLEL
• Primary Study Purpose: DIAGNOSTIC
• Blinding Strategy: SINGLE

Study Groups

LV function from cardiologist

Certified Cardiologist will access and interpreted LV function by used traditional Echocardiography then separate result into three group Preserved LV ejection fraction(EF\>50%), mildly reduce ejection fraction(EF40-49%), reduced LV ejection fraction(EF\<40%)

Group Type: ACTIVE_COMPARATOR

Easy EF

Intervention Type: OTHER

AI was integrated into the application smartphone and used smartphone camera to recorded shot VDO clip of heart ultrasound in parasternal long axis view and returned cardiac function result to user.

LV function By artificial intelligence

AI will access VDO clips in only parasternal long axis view and separate into three group Preserved LV ejection fraction(EF\>50%), mildly reduce ejection fraction(EF40-49%), reduced LV ejection fraction(EF\<40%)

Group Type: EXPERIMENTAL

Easy EF

Intervention Type: OTHER

AI was integrated into the application smartphone and used smartphone camera to recorded shot VDO clip of heart ultrasound in parasternal long axis view and returned cardiac function result to user.

Interventions

Easy EF

AI was integrated into the application smartphone and used smartphone camera to recorded shot VDO clip of heart ultrasound in parasternal long axis view and returned cardiac function result to user.

Intervention Type: OTHER

Other Intervention Names

artificial intelligence; mobile smart phone application

Eligibility Criteria

Inclusion Criteria

• Shot 5 second VDO clip of Parasternal long axis heart ultrasound recorded by smartphone Application "Easy EF" without patient identification with result of Ejection fraction that performed by certify cardiologist approved result

Exclusion Criteria

• Incomplete VDO clip (too much shaking, too shot recording)
• Lighting was inappropriate
• Inappropriate ultrasound framing
• arrhythmia (atrial fibrillation)

• Eligible Sex: ALL
• Accepts Healthy Volunteers: Yes

Sponsors

Chulalongkorn University

OTHER

Sponsor Role: collaborator

Rayong Hospital

OTHER

Sponsor Role: lead

Responsible Party

Sittiluck Wongwantanee

Principal Investigator

Responsibility Role: PRINCIPAL_INVESTIGATOR

Locations

Rayong Hospital

Rayong, , Thailand

Countries

Thailand

Other Identifiers

RayongH

Identifier Type: -

Identifier Source: org_study_id