Assessing the Efficacy of Artificial Intelligence in Left Ventricular Function Screening Using Parasternal Long Axis View Cardiac Ultrasound Video Clips

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Sittliluck Wongwantanee
Natawut Nupairoj
Thananop Kobchaisawat
Chaichana Thavornthaveekul


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 alternative 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 a smartphone application.

OBJECTIVES: To evaluate the effectiveness of AI in screening cardiac function from parasternal long axis view cardiac ultrasound video clips obtained through a smartphone application.

METHODS: The authors developed a smartphone application that could be used to collect parasternal long axis view video clips and use artificial intelligence “Easy EF” to evaluate cardiac function. Out of 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 whether AI could process the results correctly. Artificial intelligence aims to classify cardiac function into three groups: Reduced EF, Mildly Reduced EF, and Preserved LV.

RESULTS: Out of 184 test video clips, AI achieved 96% classification results. The proposed AI was able to classify Reduced EF with 97% accuracy (36 from 37 clips), Mildly Reduced EF with 71% accuracy (12 from 17 clips), and Preserved LV with 97% accuracy (129 from 131 clips) P=0.0147. Overall accuracy was 96.2% (177 from 184 clips).

CONCLUSIONS: Artificial Intelligence in the form of “Easy EF” has been demonstrated to be promising screening tool for the assessment of cardiac function from parasternal long axis video clips. However, further development is needed, particularly to enhance accuracy in the Mildly Reduced EF group. Identifier, NCT06330103

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Original Article


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