Advancing Valvular Heart Disease Screening: AI-Enhanced Auscultation and Handheld Sensing for Early Detection
Presentation by Professor Anurag Agarwal (University of Cambridge)
Abstract:
Valvular heart disease (VHD) is a significant public health challenge, with over half of cases remaining undiagnosed due to late symptom onset and the limitations of traditional stethoscope-based screening. This talk will explore two promising approaches to improve VHD screening.
First, we will discuss the development and validation of an AI-enhanced auscultation algorithm that directly predicts clinically significant VHD from electronic stethoscope recordings. This algorithm, trained on a large, diverse dataset of NHS patients, achieves a sensitivity of 98% for severe aortic stenosis and 94% for severe mitral regurgitation, significantly outperforming general practitioners in both sensitivity and specificity.
Second, we will introduce a novel handheld, multi-sensor device designed for untrained users to record high-quality heart sounds without requiring patients to undress. This device, equipped with advanced signal quality algorithms, significantly improves the likelihood of successful recordings across diverse body types and has the potential to enable low-cost, accessible screening in resource-constrained settings.
Together, these advancements offer a practical, non-invasive solution for early VHD detection, supporting timely intervention and improved patient outcomes. This talk will highlight the integration of AI, wearable technology, and health data in enhancing cardiac screening and population health.
About the speaker:
Professor Anurag Agarwal is the Head of the Fluids Group and the Acoustics Lab in the Engineering Department at the University of Cambridge. His research focuses on understanding the physical mechanisms that link diseases to bodily sounds and vibrations, and leveraging this knowledge to develop AI-driven diagnostic tools. Example applications include the detection of valvular heart disease, understanding the inaccuracies in non-invasive blood pressure measurement, and the identification of wheezing in lung conditions. By combining fundamental acoustics research with advanced machine learning techniques, Prof. Agarwal aims to create accessible, accurate, and scalable solutions for early disease detection and monitoring. A key aspect of his work is the collection and curation of high-quality clinical data, which underpins the development of robust and reliable AI models for healthcare applications.
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https://sanger.zoom.us/j/98898125575?pwd=MQOJwCliHlvrlVJcfOjD4jxtC0Vf1A.1
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