Project: Big Heart Data
Client: Kings College London
Each human heart has a unique anatomy that changes over time and can be influenced by our lifestyle and environment.
What if you could foresee how your own heart would develop and grow?
Year
2019
Sector
Medical imagery
Discipline
Parametric
3d Printing
Speculative
Storytelling
Exhibition
Ethos
Biomimetism
Personalisation
Selfcare
“Each human heart has a unique anatomy.”
Starting from a speculative exploration in the role of digital tools in the representation of our organs and our relation to personalised treatment and self-care, we could see a huge potential in 3d models to support communication with the clinical team and provide psychological support to patients.
In collaboration with CME at Kings College London and 3d printing local factory Batchworks, Big Heart Data uses low cost 3d printing and open source 3d modeling software.
“3d printed models to support the clinical team.”
Big Heart Data explores the role of 3D printing and parametric modeling in cardiology, and its potential in a personalised healthcare system. The project devises a system for creating personalized digital and 3D-printed models of hearts, which can be used to help medical doctors and facilitates communication within the medical team and patients.
Using large data sets and artificial intelligence software, computer scientists and clinicians are now aiming to make predictions about the growth and development of individual human hearts from birth through to adulthood.
Designer Salomé Bazin and researcher Pablo Lamata from the Department of Biomedical Engineering at King’s College London developed Big Heart Data to demonstrate the potential of software systems, and design, to visualise future changes in the heart.
Cellule were commissioned by the Science Gallery London to showcase Big Heart Data as part of their Spare Parts season.
Big Data
Computational Cardiology
Digital Twin
Big Data is the science of processing data that is too large, fast and complex to be analysed using traditional methods. With the advent of the internet and the internet of things, computers are dealing with extremely large quantities of data arriving in at an extremely fast rate and in a variety of complex formats (numbers, text, audio, video…). Big data seeks to capture, store and extract information from these kinds of data, with acceptable results and in an acceptable time. It englobes fields like statistical analysis and machine learning. Data analysis can help predict business trends, streamline user experiences, or build complex models of an individual’s hearts!
The paradigm shift in surgery is to plan the best healthcare provision adapted to our specific biological architecture and machinery. The combination of medical imagery with machine learning and omics science target for a better understanding of individuals as well as population health.
Computational cardiology is the use of advanced imaging, genetic screening and devices to understand heart conditions and to treat patients according to their specific pathophysiology. Cardiologists use computational models that analyse great amounts of patient-specific physiological and physical information, to reveal diagnostic information and predict clinical outcomes, which enables personalising treatment for individuals.
Scanning technologies (MRI, CT, Echocardiography) are widely used, non invasive technique to create detailed images of organs and tissue in the body using strong magnetic fields or ultrasound to create 2D or 3D imagery.
Personalised medicine is an emerging approach to patient treatment. By analysing patient-specific information about their physiology, pathology, genome and lifestyle, combined with patterns observed in the scale of populations, we are able to move toward more precise, predictable and powerful health care for individual patients. One of the practices of personalised medicine is to create digital twins of patients: digital replicas of patients based on their recorded data, which allows to predict critical information about each individual, such as their risk of developing a disease, and how they would respond to a treatment.
A rising trend is in-silico medicine (as opposed to in-vivo or in-vitro) where drugs or interventions are tested by computer simulations.
Big Data
Big Data is the science of processing data that is too large, fast and complex to be analysed using traditional methods. With the advent of the internet and the internet of things, computers are dealing with extremely large quantities of data arriving in at an extremely fast rate and in a variety of complex formats (numbers, text, audio, video…). Big data seeks to capture, store and extract information from these kinds of data, with acceptable results and in an acceptable time. It englobes fields like statistical analysis and machine learning. Data analysis can help predict business trends, streamline user experiences, or build complex models of an individual’s hearts!
The paradigm shift in surgery is to plan the best healthcare provision adapted to our specific biological architecture and machinery. The combination of medical imagery with machine learning and omics science target for a better understanding of individuals as well as population health.
Computational Cardiology
Computational cardiology is the use of advanced imaging, genetic screening and devices to understand heart conditions and to treat patients according to their specific pathophysiology. Cardiologists use computational models that analyse great amounts of patient-specific physiological and physical information, to reveal diagnostic information and predict clinical outcomes, which enables personalising treatment for individuals.
Scanning technologies (MRI, CT, Echocardiography) are widely used, non invasive technique to create detailed images of organs and tissue in the body using strong magnetic fields or ultrasound to create 2D or 3D imagery.
Digital Twin
Personalised medicine is an emerging approach to patient treatment. By analysing patient-specific information about their physiology, pathology, genome and lifestyle, combined with patterns observed in the scale of populations, we are able to move toward more precise, predictable and powerful health care for individual patients. One of the practices of personalised medicine is to create digital twins of patients: digital replicas of patients based on their recorded data, which allows to predict critical information about each individual, such as their risk of developing a disease, and how they would respond to a treatment.
A rising trend is in-silico medicine (as opposed to in-vivo or in-vitro) where drugs or interventions are tested by computer simulations.
