Doctors commonly use MRI (magnetic resonance imaging) scans to see parts of the body that aren’t easily visible through methods like X-rays or ultrasounds.

Then, it becomes easier to make diagnoses or perform examinations for other reasons.

Now, it’s possible that machine learning and artificial intelligence (AI) could make MRIs even more useful than before — here are four ways how.

1. Producing High-Quality Images With Less Data

Although MRIs are undeniably beneficial for the people who need them, they can take up to an hour depending on the part of the body being imaged.

Moreover, individuals must stay completely still during that time while being inside a chamber that could cause claustrophobia. Add in the fact that patients might be in severe pain during an MRI, and it’s not hard to see why everyone would appreciate if the MRI process were shorter.

The FastMRI project includes insights from researchers at New York University (NYU) and Facebook who believe machine learning could produce adequate MRI images in less time than currently used methods.

Typically, an MRI machine takes images 2D images and stacks them to make 3D versions. But, scientists think it’s possible for machine learning to enhance less-detailed MRI images by intelligently filling in the gaps — eventually cutting down MRI scan times

The current goal is to run the MRI 10 times faster than usual but achieve an image quality level on par with conventional methods.

2. Using Predictive Algorithms to Minimize Breakdowns

MRI machines feature numerous parts that must work together for best results, including a magnet that must stay cool. It makes a magnetic field strong enough to make protons in the body align with the field. A radiofrequency current gets sent through the patient to make the protons go against the magnetic field’s pull.

Then, the radiofrequency gets turned off, and sensors on the MRI equipment detect the energy emitted as the protons realign. The faster the realignment happens, the brighter the resultant MRI image.

Medical chillers are specially designed to endure load surges in addition to keeping the temperature constant during all other daily operation loads. High-quality parts are up to the task, but MRI machine failures can still occur.

Many companies in the manufacturing sector use AI tools that predict maintenance needs before total breakdowns happen. The associated executives understand even an equipment malfunction lasting a few minutes can disrupt operations and cost tens or even hundreds of thousands of dollars.

Similarly, a broken MRI machine becomes costly and inconvenient for hospitals. AI algorithms could make predictions about maintenance for proactive prevention.

3. Relying on Smarter MRIs to Aid in Better Decision Making

Some hospitals use MRI data before, during and after operations so surgeons can plan how to proceed or determine the extent of a tumor, for example. At Boston’s Brigham and Women’s Hospital, an MRI is inside the operating room as part of a larger imaging setup called the Advanced Multimodality Image Guided Operating Suite (AMIGO).

The staff at Brigham and Women’s also added a mass spectrometer to its AMIGO equipment. Machine learning analyzes data collected by that component and compares it to a decade worth of historical data about brain tumors while looking at a segmented MRI image.

Then, surgeons benefit from better insights about patients’ tumors. As a result, people may undergo fewer future operations because the first attempts are maximally successful.

Additionally, an Indian startup designed algorithms with deep learning and other advanced intelligent technologies.

The software containing those algorithms works with any MRI machine, CT scanner or X-ray machine. It screens for abnormalities and assesses their severity, giving results achieved much quicker than through older methods.

Also, some algorithms are trained on over a million images. That means this process could help physicians feel more confident when making diagnoses, thereby reducing potential mistakes and improper treatment plans.

4. Experimenting With AI to Assess the Extent of Brain Damage

Medical technicians perform functional MRIs (fMRIs) to measure brainwave activity.

Researchers in China developed an AI algorithm they report works in conjunction with fMRIs to predict the likelihood of people with severe brain damage regaining consciousness.

It works by assessing the level of awareness a person has and then using that information and factors related to disorders of consciousness (DOCs) to give a suggested prognosis for recovery.

Emerging Technologies Make MRIs Even More Worthwhile

This brief overview of using machine learning and AI in MRI applications shows the efforts indicate plenty of promise.

As technology improves, the related advancements could make MRIs better than ever for patients and care providers alike.