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Detection of heart failure with preserved ejection fraction (HFpEF) remains challenging due to indeterminate imaging outputs and discordant clinical features. Existing guidelines and clinical algorithms often produce non-diagnostic outcomes, especially in complicated cases. To address this, we developed an artificial intelligence (AI) model to analyze a single echocardiographic video clip for HFpEF detection. This study aimed to assess the clinical utility of the AI HFpEF model in comparison to the HFA-PEFF and H2FPEF scores using decision curve analysis, focusing on the net benefit of patient management decisions based on the output of clinically validated algorithms and/or the AI HFpEF model. We developed a three-dimensional convolutional neural network and trained it on apical four-chamber transthoracic echocardiogram video clips to classify patients with HFpEF (cases) and without HFpEF (controls). Decision curve analysis was conducted to compare the net benefit of patient management decisions based on the output of the HFA-PEFF, H2FPEF scores, and/or the AI HFpEF model, assuming a 20% prevalence of HFpEF in the target population (cardiology/heart failure clinic) and a threshold probability of 30%. The model was independently tested in 8 centers across 4 states (646 cases, 638 controls). The results demonstrated high sensitivity (87.8%; 84.5-90.9%) and specificity (81.9%; 78.2-85.6%) which was sustained across clinically relevant subgroups. Decision curve analysis revealed that managing patients based on the combined information from the HFA-PEFF or H2FPEF scores and the AI HFpEF model resulted in 5 to 6 more patients with HFpEF (per 20 patients) being managed appropriately compared to managing based on the HFA-PEFF or H2FPEF scores alone, (Figure 1). The AI HFpEF model, when combined with the HFA-PEFF or H2FPEF score, may offer an improved approach for managing patients with HFpEF. Decision curve analysis highlights the net benefit of incorporating the AI model in clinical decision-making, thereby demonstrating the potential for enhancing management and care for patients with HFpEF. [ABSTRACT FROM AUTHOR]