In heart research, machine learning techniques are increasingly used to analyse the shapes and movements from the human heart from imaging data. Cardiac magnetic resonance (CMR) is the best way to look at the heart in detail for diagnosing cardiovascular disease. However, existing analysis techniques for CMR images only report simplistic volumetric measures from the images to describe the heart structure and function. These measures are not able to describe the regional and subtle differences of the 3D heart shape and motion.

Dr Wenjia Bai explains: In an analogy, if we describe a person just by their height, it is not able to reflect the 3D shape of the human body and different shape patterns across the population. Therefore, a better method to describe the 3D heart shape and motion would greatly enhance our understanding about the variations of the shape and motion, and discover patterns related to cardiovascular disease. The Imperial team created a novel AI model called MeshHeart that builds detailed 3D models of the heart’s structure and movement throughout a heartbeat.

This uses a type of deep learning called graph convolutional networks to understand the shape of the heart and a transformer model to capture how it changes over time. He system was trained on a large dataset of over 38,000 heart scans, allowing the model to understand what a healthy heart typically looks like for different ages and sexes. Importantly, the model can generate a personalised normal heart model based on a particular individual’s clinical information. By comparing an individual’s actual heart model to their personalised healthy reference, the system can therefore detect differences that may indicate underlying heart conditions or potential health risks.

Source: https://www.imperial.ac.uk/news/264275/ai-model-generates-personalised-heart-animations/