Novel AI-Driven Heart Failure Diagnosis

Using artificial intelligence (AI), researchers have developed a new way to identify patients with heart failure who will benefit from treatment with beta-blockers. This finding is published in the journal The Lancet. The prevalence of atrial fibrillation (AF) is expected to double in the coming decades so better identification of patient subgroups that could benefit from therapy is critical to address this unsustainable burden on healthcare services.

Led by the cardAIc group, a multi-disciplinary team of clinical and data scientists at the University of Birmingham and the University Hospitals Birmingham NHS Foundation Trust, aiming to integrate AI techniques to improve the care of cardiovascular patients conducted a new study.

For the study, 15,669 patients with heart failure and reduced left ventricular ejection fraction (low function of the heart's main pumping chamber) were involved. Researchers used a series of AI techniques to “deeply interrogate” data from clinical trials.

Of the patients, 12,823 of which were in normal heart rhythm and 2,837 of which had AF, a heart rhythm condition commonly associated with heart failure that leads to worse outcomes.

The AI-based approach combined neural network-based variational autoencoders and hierarchical clustering within an objective framework, and with detailed assessment of robustness and validation across all the trials.

The results showed that the AI approach could take account of different underlying health conditions for each patient as well as the interactions of these conditions. This will help to isolate patient’s response to beta-blocker therapy.

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Development of these new AI approaches is important for improving the care for heart failure patients. In the future this could even lead to personalized treatment for each individual patient, taking account of their particular health circumstances to improve their well-being.

Source-Medindia