Introduction
This application aimed to establish a rapid prescreening tool for heart failure with preserved ejection fraction (HFpEF) by using artificial intelligence (AI) techniques to detect abnormal echocardiographic patterns in structure and function based on intrabeat dynamical changes in the left ventricle (LV) and the left atrium (LA). Although diagnostic criteria for HFpEF have been established, rapid and accurate assessment of HFpEF through echocardiography remains challenging and highly desirable. In total, 1263 individuals with HFpEF and 1041 individuals without HFpEF were included. The participants' four-chamber view images were extracted from the echocardiography files and randomly separated into the training (n = 1865), validation (n = 207), and internal testing dataset (n = 232). Also, an external testing dataset was provided from another hospital (n= 315). The intrabeat dynamics of the geometrical measures were extracted frame by frame from the image sequence to train the AI model. In the internal testing dataset, the accuracy, sensitivity, and specificity of the best AI model for detecting HFpEF were 0.91, 0.96, and 0.85, respectively; in the external testing dataset were 0.90, 0.90,and 0.89. Our AI system is a rapid, time-saving, and accurate prescreening method that incorporates the novel intrabeat dynamics concept to facilitate HFpEF diagnosis. In addition to the classification of the diagnostic outcome, such an approach can automatically generate valuable quantitative information to assist clinicians in the diagnosis of HFpEF.
Features / strengths
This product utilizes AI-assisted fast processing algorithm (within 1 minute) from single beat cardiac ultrasound dynamic image one single four-chamber plane to complete a rapid pre-screening of preserved ejection fraction heart failure (HFpEF), which remains a clinically challenging disease. As a reference for clinical diagnosis and treatment and medical decision-making, this platform has the advantage of having a very high diagnostic accuracy while also taking into account clinical convenience with high efficiency.
Specification in detail
Under development
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