Incorporating Machine Learning into Medical Sensor Technologies

1. Incorporating Machine Learning into Medical Sensor Technologies Machine learning is all the rage these days in the video game arena. Along with its cousin, artificial intelligence (AI), machine learning is transforming the way developers write the code that powers everything from smartphones to game consoles. Is there a place for machine learning in the medical field? Absolutely. Incorporating machine learning into medical sensor technologies is one of the things we do at Rock West Solutions. We take great pride in being at the cutting edge of our industry, serving the healthcare sector as well as government agencies and manufacturers. As we see it, there are plenty of opportunities for machine learning to be married with medical sensors. In this post, we discuss two such opportunities. Before we do though, a distinction must be made between machine learning and artificial intelligence. They are two distinct entities. Machine Learning vs. Artificial Intelligence Machine learning is a very simple principle that has been implemented by software developers for decades. It is the ability of a software system to use data to ‘teach itself’ how to better respond to future input. IBM’s chess playing Deep Blue computer of the 1990s is an excellent example of machine learning. Artificial intelligence is a bit different. AI is the ability of a computer system to go out and get information, then parse that information in order to use the most valuable data points to initiate machine learning. So where machine learning is dependent on the data fed to it, AI goes and gets the data it needs. True AI does not yet exist in its purest form. Therefore, people who talk about AI and its practical applications are almost always referring to machine learning. With that said, let us talk about machine learning and medical sensor technologies.

2. Medical Imaging Opportunities Medical imaging and diagnostics is currently an exciting area for machine learning. Consider, for example, a gastrointestinal tracking device that works more or less like a GPS for the gut. Being able to embed such a tracking device into a piece of medical imaging equipment would make it easier for doctors and diagnostic technicians to make sense of the data they are looking at. Right now, medical imaging is not an exact science. We can get the images clearly enough but diagnosing what those images mean is not so cut and dried. We can change that by introducing machine learning into the medical sensor technologies currently under development. Machine learning will eventually be able to give diagnostic equipment the ability to interpret data instead of relying so heavily on human experience and opinion. Robotic Surgery Opportunities Another exciting area is that of robotic surgery. We can take the same principles behind the gastrointestinal tracking device and use it to create better equipment for robotic surgeries. In this area, precision is key. Robotic surgery has already been used on a limited basis to perform remote surgical procedures by which the doctor manipulates equipment located in a faraway location using computerized controls back home. But what if robotic surgical devices could do what they need to do without direct human manipulation? That’s where machine learning becomes indispensable. A computerized surgical robot with built-in machine learning could eventually be used to perform routine procedures like tonsillectomies and appendectomies. And even if surgical robots never become completely autonomous, they will at least improve what we now offer by expanding opportunities for remote surgery. Machine learning is driving what we do here at Rock West Solutions. It is also driving the healthcare industry forward with better medical sensor technologies that will eventually make all of us healthier. For example, at Rock West Solutions we are in the process of collecting an enormous database from our gastrointestinal tracking devices applied to many patients. This large database will be the foundation to perform supervised and unsupervised machine learning to allow whole transit analyses to diagnose GI motility disease. The collected database records contain a rich source of never-before seen physiological information with second-by-second motility information. The coupling of machine learning to this data with a gastroenterologist’s insight will greatly enhance the capabilities of the doctors to improve diagnoses’ speed and accuracy. As our processing of the data sets matures, machine learning will identify diseases that are currently hard to diagnose, such as pelvic dyssynergia or other disease issues such as diabetic neuropathy and many more. This machine learning with Rock West Solutions data collection promises to greatly improve the gastroenterology diagnoses.