As Americans continue to become more conscious of their health and take greater ownership over their personal healthcare. Now tech companies are jumping in to provide more and better resources and tools. I was recently introduced to a company that uses four decade old MRI technology, but is reinventing it by making it available to consumers in modern ways. Marcus Foster is founder and CEO of Klarismo, a company that builds 3D body profiles through an automated quantitative image analysis pipeline. What is automated quantitative image analysis? Foster gave me a break down on how this tech will shape the future of health-conscious consumers:
The Importance of Understanding Body Composition
Understanding body composition in detail allows for the definition of Image Derived Phenotypes (IDP). Knowing precise volumetric measurements of different tissue types and organs. We can begin to understand what genetic and environmental factors contribute to certain phenotypes. Foster explains that with the advances in MRI technology, we can now correlate the volume of certain muscles with other observations (e.g. a person’s mobility or strength). To understand how changes in muscle volume influence other areas.
Significance of MRI Profiles Over Time
Imagine if we could give someone a detailed prediction and 3D rendering of what their body would look like in five years time based on different lifestyle choices. What they will look like at 40 if they don’t change anything, versus what they would look like if they started cycling to work every day and stopped eating wheat, or meat, or sweets, etc. He imagines people regularly scheduling MRI scans to keep track of physical changes to their bodies over time – either as a result of ageing or lifestyle changes such as diet and exercise.
The Future of MRI Tech is Machine Learning
Foster is hopeful that the growing demand for medical imaging will lead to a reduction in the cost of the equipment and maintenance. The established methods to perform quantitative analysis of MRI scans and to understand changes between two scans all involve a lot of manual work. Which simply does not scale when you want to look at thousands of images simultaneously. Foster says his approach has always been focused on eliminating any involvement of people in the analysis. By manually annotating a large sample data set of whole body scans and training machine-learning models how to read the data. It has become possible to rapidly quantify an ever-growing number of features in the body. This computation is performed in the cloud it is highly scalable and cost effective.
Additional Applications for this Research
Klarismo’s technology is enabling large, population-level imaging studies to perform quantitative analysis quickly and reliably.
