Publications by authors named "Joshua Finer"
Circulation
September 2024
Article Synopsis
- A novel deep learning (DL) system was developed to enhance the interpretation of transthoracic echocardiography (TTE) for assessing the severity of mitral regurgitation (MR) by integrating multiple video assessments.
- The system was tested with a large dataset (over 61,000 TTEs) and showed high accuracy in classifying MR severity, achieving exact accuracy rates of 82% for internal and 79% for external test sets.
- Most misclassifications occurred between none/trace and mild MR categories, and the use of multiple TTE views improved classification accuracy.
J Am Coll Cardiol
August 2022
Article Synopsis
- Valvular heart disease, specifically aortic stenosis (AS), aortic regurgitation (AR), and mitral regurgitation (MR), is a significant but often underdiagnosed issue that deep learning analysis of electrocardiograms (ECGs) can potentially address.
- The study involved 77,163 patients and developed deep learning algorithms to accurately identify moderate to severe cases of AS, AR, and MR using ECG data, achieving good performance metrics, especially for AS (AU-ROC: 0.88).
- Results suggest that deep learning could be integrated into screening programs for valvular heart disease, showing high sensitivity and specificity, particularly at low disease prevalence rates.
