A recent study at the University of Dundee’s School of Medicine demonstrated the potential of artificial intelligence in identifying individuals at risk of heart failure. By analysing echocardiographic images with AI, researchers could detect heart anomalies more effectively, leading to improved early diagnosis and patient outcomes.
AI Study Offers Hope for Early Heart Failure Detection
Researchers at the University of Dundee’s School of Medicine have leveraged artificial intelligence (AI) to help identify patients at risk of heart failure. The study utilized AI to scan echocardiographic images, identifying structural and functional heart anomalies from population-based electronic health records. Data was sourced from the Scottish Health Research Register and Biobank (SHARE) and included 15,000 patient records, narrowed down to 578 participants.
Professor Chim Lang led the research, showing that AI-enhanced heart scans provided more detailed measurements than traditional scans, potentially improving the early diagnosis and surveillance of heart failure. Prof Lang emphasized the study as a significant advancement in using AI for large-scale identification of heart failure patients, aiming to enhance clinical trial efficiencies and patient outcomes.
The findings were published in the journal ESC Heart Failure and the project was supported by Roche Diagnostics International and Us2.ai.
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