Clinical trial NCT06330103: Efficacy of AI EF Screening by Using Smartphone Application Recorded PLAX View Cardiac Ultrasound Video Clips
Official title: Assessing the Efficacy of Artificial Intelligence in Left Ventricular Function Screening Using Parasternal Long Axis View Cardiac Ultrasound Video Clips
Study type: INTERVENTIONAL
Overall status: COMPLETED
Phase: NA
Conditions: Heart Failure
Heart Failure With Reduced Ejection Fraction
Cardiac Failure
Echocardiography
Artificial Intelligence
Lead sponsor: Rayong Hospital
Sponsor class: OTHER
Collaborators: name: Chulalongkorn University
First posted: 2024-03-26
Enrollment: 923
Sex: ALL
Standard ages: CHILD
ADULT
OLDER_ADULT
Accepts healthy volunteers: yes
Allocation: NON_RANDOMIZED
Intervention model: PARALLEL
Intervention model description: 923 samples that were evaluated for LVEF by certified cardiologists, 739 clips were used to train AI, while the remaining 184 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.
Primary purpose: DIAGNOSTIC

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.

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)

Primary outcomes:
measure: efficiency of AI in screening left ventricular cardiac function by use smartphone application
timeFrame: 3 month
Secondary outcomes:
Other outcomes:
Locations:
city: Rayong
country: Thailand
facility: Rayong Hospital
Study officials:
References: