Aug 10, 2023

Disclosures: This study received grant support from Ultromics Ltd. Akerman is an employee of Ultromics Ltd. Gevaert reports receiving lecture/advisory board fees paid to his institution by Abbott, AstraZeneca, Boehringer Ingelheim, Menarini and Novartis. Please see the study and editorial for all other authors’ relevant financial disclosures. ADD TOPIC TO EMAIL ALERTS Receive an email when new articles are posted on Please provide your email address to receive an email when new articles are posted on . Please try again later. If you continue to have this issue please contact customerservice@slackinc.com . Back to Healio Key takeaways: An AI model has been designed to diagnose HF with preserved ejection fraction from a single transthoracic echocardiogram video clip. The algorithm demonstrated high levels of sensitivity and specificity. Artificial intelligence demonstrated strong diagnostic performance in the detection of HF with preserved ejection fraction using a single transthoracic echocardiogram video clip, according to data published in JACC: Advances. “ Recent work in artificial intelligence (AI) computer vision techniques offer great promise that computational methods can better interpret the vast amount of information that exists within medical data including images. Whereas recent AI studies have combined clinical parameters and manual echocardiographic measurements to classify diastolic dysfunction and HFpEF, fewer have used echocardiographic images,” Ashley P. Akerman, PhD, senior clinical research scientist at Ultromics Ltd. in Oxford, U.K., and colleagues wrote. “Development of an approach using this simple input might obviate the need for complex Doppler assessment, provide supporting information when traditional measures are nondiagnostic, or limit data requirements when such data collection is not feasible.” An AI model has been designed to diagnose HFpEF from a single transthoracic echocardiogram video clip. Image: Adobe Stock Akerman and colleagues trained and validated a 3D convolutional neural network AI using apical four-chamber transthoracic echocardiogram (TTE) video clips from 2,971 patients with HFpEF and 3,785 controls. “This view was selected because it includes much information (chamber sizes, wall thicknesses, annulus motion, etc) and is routinely acquired in imaging protocols,” the researchers wrote. AI training, validation and performance The AI model had an area under receiver-operating characteristic curve (AUROC) of 0.97 (95% CI, 0.96-0.97) in training and an AUROC of 0.95 (95% CI, 0.93-0.96) in validation. In independent testing conducted using 646 cases of HFpEF and 638 controls, the AI demonstrated a sensitivity to detect HFpEF of 87.8% (95% CI, 84.5-90.9) and a specificity of 81.9% (95% CI, 78.2-85.6) with high repeatability and reproducibility, according to the researchers. Furthermore, performance was assessed in an independent dataset in which the diagnostic value of the AI was compared with the previously validated Heart Failure Association-Pretest Assessment, Echocardiographic and Natriuretic Peptide Score, Functional Testing, and Final Etiology (HFA-PEFF) and Heavy, Hypertensive, Atrial Fibrillation, Pulmonary Hypertension, Elder and Filling Pressure (H2FPEF) scores. The AI correctly detected HFpEF among 73.5% of 701 indeterminate outputs from the HFA-PEFF score and 73.6% of 776 indeterminate outputs from the H2FPEF score, according to the study. During a median follow-up of 2.3 years, the researchers observed increased mortality risk among patients with HFpEF as determined by the AI compared with patients in whom the AI did not report HFpEF (HR = 1.9; 95% CI, 1.5-2.4). “The ability to automatically detect HFpEF with limited clinical information has important practical ramifications, particularly for screening in centers without the time or expertise to complete diagnostic quality diastolic assessment, resulting in indeterminate or unclear clinical diagnoses,” the researchers wrote. “Combined, the technical and clinical feasibility demonstrated with this model could result in faster patient access to effective pharmacological therapy.” AI as a ‘second read’ when other metrics uncertain In a related editorial, Andreas B. Gevaert, MD, PhD, postdoctoral researcher in the department of genetics, pharmacology and physiopathology of heart, blood vessels and skeleton at the University of Antwerp, and colleagues discussed the AI model’s performance compared with the two validated scoring systems and the algorithm’s place in clinical decision-making. “The better performance of the model compared to the scoring systems needs to be interpreted with caution,” the authors wrote. “Natriuretic peptide levels are an essential part of the HFA-PEFF score. However, these were only available in 12% of the validation population, implying that a diagnosis of HFpEF could not be established anyway in the majority of patients using this score. Both scoring systems mandate further (exercise) testing in patients labeled as ‘intermediate’ risk, which was not accounted for in the current study. “As the authors imply in their paper, the ideal use for this algorithm (or similar AI applications) would be as a ‘second read’ when other clinical or echocardiographic metrics remain uncertain regarding a diagnosis of HFpEF,” they wrote. “AI could serve as a ‘decision maker’ in intermediate or indeterminate cases and/or a ‘gatekeeper’ for advanced exercise testing to enhance early HFpEF diagnosis and enable timely treatment.” Reference: