Publications by authors named "Mihaela Porumb"
Article Synopsis
- Researchers used AI to analyze echocardiogram video clips to identify patients with heart failure with preserved ejection fraction (HFpEF), a condition where heart muscle works well but has filling issues.
- The AI model was trained on nearly 6,000 cases, showing strong accuracy in distinguishing between HFpEF and non-HFpEF patients, achieving an area under the curve of 0.97 in training and 0.95 in validation.
- The AI successfully reclassified a majority of indeterminate results from existing clinical scores during testing, indicating its potential to improve heart failure diagnosis over traditional methods.
Objectives: The purpose of this study was to establish whether an artificially intelligent (AI) system can be developed to automate stress echocardiography analysis and support clinician interpretation.
Background: Coronary artery disease is the leading global cause of mortality and morbidity and stress echocardiography remains one of the most commonly used diagnostic imaging tests.
Methods: An automated image processing pipeline was developed to extract novel geometric and kinematic features from stress echocardiograms collected as part of a large, United Kingdom-based prospective, multicenter, multivendor study.
Tracking the fluctuations in blood glucose levels is important for healthy subjects and crucial diabetic patients. Tight glucose monitoring reduces the risk of hypoglycemia, which can result in a series of complications, especially in diabetic patients, such as confusion, irritability, seizure and can even be fatal in specific conditions. Hypoglycemia affects the electrophysiology of the heart.
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