Computational Medicine: 2024-2025
Lecturer | |
Degrees | Schedule C1 (CS&P) — Computer Science and Philosophy Schedule C1 — Computer Science |
Term | Hilary Term 2025 (20 lectures) |
Overview
This course is intended for students who want to understand modern computational medicine from methodologies to impact through industry and clinical integration. With a focus on Computational Cardiology, the course will provide an introduction to challenges and computational techniques including modelling & simulation & machine learning, and will cover fundamental methods for constructing and exploiting patient-specific multiscale and multi-physics human hearts modelling & simulation in health and disease and their use for therapy development and testing of therapies. It will cover both functional and anatomical modelling from multimodality data, and important concepts broadly applicable to all areas of medicine in industry, academia and regulatory practice such as the Digital Twin vision, In Silico Trials for therapy evaluation and verification, validation and uncertainty quantification.Learning outcomes
Upon completing this course, the student will understand:
1. the need, scope and key concepts in computational medicine through the exemplar of computational cardiology;
2. computational methods for medicine through functional and anatomical modelling in cardiology, and the key issues that arise when integrating them with experimental and clinical methods;
3. mathematical and computational concepts in order to tackle the major challenges of physically realistic multiscale multiphysics simulations based on multi-modality datasets;
4. methods and challenges in the implementation for in silico trials for medicines and other therapeutic solutions;
5. how to formulate a natural phenomenon such as cardiac physiology and arrhythmias in terms of complex dynamics;
6. gain an understanding of the importance of guidelines for verification, validation and uncertainty quantification;
7. identify and understand a research-level topic at the intersection between medicine and computer science.
Prerequisites
The course assumes basic knowledge of discrete/continuous mathematics, probability and statistics. However, all concepts needed will be covered during the course.
Synopsis
Introduction to Computational Medicine. Motivation. Need for computer science in medicine. Computational tools in medicine: data processing, computer modelling and simulation. The concept of the ‘Digital Twin’ and the ‘Virtual Physiological Human’. Computational cardiology as an exemplar.
Cardiac anatomy and physiology. Overview of physiology and anatomy underlying cardiac function, with details on cellular electrophysiological function and contraction, as well as basic principles of cardiac mechanics and cardiac macroscopic anatomy. Structure morphology in normal and pathological cases. Microscopic structure and molecular organisation.
Interface with experimental and clinical cardiology. Role of modelling and simulation in cardiology. Experimental and clinical methods in clinical cardiology: limitations and opportunities.
Modelling anatomy. Cardiac imaging, computational pipelines for image segmentation and registration using deep learning, clinical applications.
Modelling and simulation of the human cardiac cellular dynamics. Theory underpinning how to mathematical model and simulate the physiological processes underpinning cellular function in the human heart. From deterministic to population in silico studies of human cardiac cellular function in health and disease.
Modelling and simulation of human whole-heart dynamics. Mathematical theory underpinning models of electrical propagation. Comparison of propagation models and forward calculation methods. Patient-specific simulations of whole-organ electrophysiology.
Computational Biomechanics. Invited lecture covering computational medicine approaches in biomechanics with a focus on the musculo-skeletal system.
The ‘Digital Twin’. Synergies between machine learning and mechanistic modelling & simulation towards the Digital Twin vision in medicine. Application and impact in cardiology.
In silico trials for medical therapy evaluation. Importance of modelling and simulation in the development of novel therapies, construction of virtual cohorts, conceptual framework for its regulatory use, key examples of impact of in silico trials in therapy development in medicine, implications for the replacement of animals in research.
Data Challenges & Opportunities in Medicine. Ethical, practical and policy challenges around data and their impact and opportunities for Computational Medicine.
Verification, validation and uncertainty quantification for computational medicine. Framework, guidelines and methods for VVUQ in computational medicine. Outlook.
Syllabus
Computational approaches for anatomical modelling of human hearts from clinical images. Multiscale modelling and simulation of the human heart from subcellular to whole-organ dynamics. Complex dynamics theory in cardiology. The Digital Twin in medicine. In silico trials for virtual testing of therapies. Verification, validation and uncertainty quantification.
Taking our courses
This form is not to be used by students studying for a degree in the Department of Computer Science, or for Visiting Students who are registered for Computer Science courses
Other matriculated University of Oxford students who are interested in taking this, or other, courses in the Department of Computer Science, must complete this online form by 17.00 on Friday of 0th week of term in which the course is taught. Late requests, and requests sent by email, will not be considered. All requests must be approved by the relevant Computer Science departmental committee and can only be submitted using this form.