AI Tool Offers New Hope for Diagnosing Advanced Heart Failure

Mar 23, 2026, 2:45 AM

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Recent research indicates that artificial intelligence (AI) techniques applied to cardiac ultrasound data may revolutionize the identification of patients suffering from advanced heart failure. This study, conducted by a collaborative team from Weill Cornell Medicine, Cornell Tech, and NewYork-Presbyterian, offers promising solutions for a condition often overlooked due to complex diagnostic requirements.

Currently, advanced heart failure is diagnosed through cardiopulmonary exercise testing (CPET), a process that necessitates specialized equipment and trained personnel, typically found only in larger medical facilities. Due to these limitations, only a small fraction of the estimated 200,000 individuals with advanced heart failure in the United States receive appropriate care each year.

The new AI method, detailed in a study published on March 3 in npj Digital Medicine, predicts the critical CPET measure of peak oxygen consumption (peak VO2) with high accuracy using routinely collected ultrasound images and electronic health records, thereby alleviating the diagnostic bottleneck.

"This opens up a promising pathway for more efficient assessment of patients with advanced heart failure using data sources that are already embedded in routine care," stated Dr Fei Wang, the study's senior author and an associate dean for AI and data science at Weill Cornell Medicine.

The research is part of the Cardiovascular AI Initiative, which aims to leverage AI to enhance heart failure diagnosis and management. This initiative combines the expertise of over 40 heart failure specialists with AI developers at Cornell Tech and Weill Cornell Medicine, highlighting a collaborative approach that merges clinical needs with technological advancements.

The AI model developed by Dr Wang's team, which includes lead authors Dr Zhe Huang and Dr Weishen Pan, employs a multimodal, multi-instance machine learning approach. This model can analyze various data types, including dynamic ultrasound images of the heart and corresponding electronic health records.

The study trained the AI model on deidentified data from 1,000 heart failure patients at NewYork-Presbyterian/Columbia University Irving Medical Center. Subsequently, it was tested on 127 additional patients, showing an impressive overall accuracy of approximately 85% in predicting peak VO2. This level of accuracy suggests substantial potential for clinical applications.

Plans for upcoming clinical studies are already underway, aimed at achieving US Food and Drug Administration approval for routine clinical use of this AI tool. Dr Nir Uriel, director of advanced heart failure and cardiac transplantation at NewYork-Presbyterian, emphasized that the ability to identify previously overlooked advanced heart failure patients could significantly enhance clinical practices and improve patient outcomes.

The study's findings mark a significant step forward in the application of AI in healthcare, particularly in the field of cardiology. The integration of AI into clinical workflows not only holds the potential for better diagnostic accuracy but also promises to enhance the quality of life for many patients who struggle with advanced heart failure.

In conclusion, the innovative AI-powered approach to diagnosing advanced heart failure showcases the transformative potential of technology in medicine. As this research progresses towards clinical implementation, it could pave the way for broader advancements in cardiovascular care, ultimately benefiting countless patients in need of timely and effective treatment.

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