The Royal Society: Building a Multimodal Digital Heart with AI

Overview

Oxford University fellow Obi Banerjee presents a compelling vision for a multimodal digital heart that exists in the digital domain as a digital twin of the patient. The talk explains why integrating multiple data streams and imaging modalities with artificial intelligence could transform cardiovascular diagnosis and treatment.

Key Idea

By creating a real-time, multimodal digital heart model, clinicians could simulate interventions on the digital twin before applying them to the patient, potentially reducing risk and personalizing care.

Introduction: What is a digital heart

Obi Banerjee describes the digital heart and digital twin concepts, highlighting how a true digital twin is more than a static image. A living digital heart in the digital domain can be controlled, modified, and used to test interventions before applying them to the physical patient.

Why multimodality matters

The talk uses a millennial framing to explain that doctors currently integrate diverse data types (angiography, MRI, ECG) to diagnose and treat heart disease. The goal is to combine these separate pieces into a single, coherent 3D, time-resolved model of the heart and its vasculature, enabling a fuller understanding of pathology and better decision making.

From data to diagnosis: AI in action

Banerjee outlines how machine learning can automatically identify vessels in imaging data, perform motion correction across views, and reconstruct a complete 3D vascular tree in real time. He emphasizes the potential of AI to match or exceed human performance in vessel segmentation and to provide quantitative metrics of blockage severity without additional invasive steps.

Applications in imaging modalities

The presentation covers coronary angiography, cardiac MRI, and body-surface electronics (ECG). The aim is to fuse these data streams into a unified model that also accounts for heart motion and anatomical variability across patients, moving toward a practical clinical navigation and pre-treatment planning tool.

Clinical translation and challenges

Banerjee discusses clinical trials, ethical considerations, and data governance. He stresses consent, anonymization, and the balance between population-level insights and individual patient care. Barriers include generalizability across diverse populations and the need for robust, multi-country data sources such as UK Biobank and the China Biobank to validate models in real-world settings.

Team and support

The talk closes with an acknowledgement of the interdisciplinary team and Royal Society support that has enabled the work, inviting clinicians and engineers to engage with the research in ongoing demonstrations and events.