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Abstract #4273

Automated left atrial function analysis using AI is a stronger predictor of survival than physician-measured left ventricular ejection fraction

Hoi Ching Cheung1, Sameer Zaman1, Kavitha Vimalesvaran1, Kelvin Chow2, Peter Kellman3, Hui Xue4, Rhodri Davies5, Graham D Cole1, Charlotte Manisty5, James C Moon5, and James P Howard1
1Imperial College London, London, United Kingdom, 2Siemens Healthineers, Chicago, IL, United States, 3National Institutes of Health, Bethesda, MD, United States, 4Microsoft, Richmond, WA, United States, 5University College London, London, United Kingdom

Synopsis

Keywords: Analysis/Processing, Diagnosis/Prediction

Motivation: Left ventricular function is a strong predictor of survival, but atrial function is less well understood.

Goal(s): Assessing atrial function is time-consuming and labour-intensive, requiring manual tracing of the atrial borders frame-by-frame.

Approach: We developed an artificial intelligence tool capable of automating this process and can run on scanners in real time.

Results: We found that the proportion of blood ejected by the left atrium with each beat (left atrial ejection fraction) strongly predicts survival in a large cohort of patients, beyond and incremental to similar established left ventricular measures.

Impact: Atrial function measured automatically using inline AI is a strong and incremental predictor of patient survival. This enables new biomarkers to be easily translated into clinical workflow for improved patient care.

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