Throughout the years, there have been numerous applications of Artificial Intelligence in various industries. Now, the application of AI in the field of medical imaging is no longer just a concept. In fact, through deep learning, it’s now possible to use AI in clinical practice to assist medical professionals in diagnosing conditions with the use of X-rays, CT scans, and MRI.
Deep learning is a branch of AI that takes advantage of artificial neural networks. Through the use of deep learning, numerous innovations in the field of medical imaging will pave the way for better quality and faster results that would aid doctors in their work.
Deep Learning for Better Image Reconstruction
Following the success of deep learning in a wide variety of applications throughout numerous industries, neural-network-based machine learning techniques can now finally be used to accelerate MRI results.
Applying deep learning algorithms to the MRI process for image reconstruction opens up a lot of possibilities that were not previously available using traditional reconstruction methods. Shorter scan times and better image quality are just some of the things that could result from this application. Today, AI can finally be incorporated seamlessly into the clinical practice without putting additional work for the radiologist.
Machine Learning vs. Deep Learning
Through machine learning, you are able to give computers the ability to solve problems by learning from experiences. The end goal for the use of machine learning is to create mathematical models that are possible to be trained by feeding it data to produce useful results. An optimization algorithm is then used to teach the model how to produce accurate predictions from the data it was given.
Deep learning, on the other hand, is a bit more advanced than machine learning. It consists of an artificial neural network, which is the closest thing we have to a functioning human brain. Data enters the network and is then transformed as it flows through. The network can also be trained to deliver useful predictions by identifying patterns in the data—a facet that machine learning isn’t capable of doing.
The Benefits of Using Deep Learning Reconstruction
Today healthcare providers generate and capture large amounts of data at a pace that traditional methods have never achieved before. The data collected contains extremely valuable signals and information that’s crucial to getting accurate results in an MRI.
This is where deep learning comes in to improve the process further. The result is more seamless integration, thorough analysis, and making predictions on large, heterogeneous data sets. Some of the benefits you can get from deep learning application to the MRI process include:
- One-dimensional biosignal analysis;
- Predicting medical events, like seizures and cardiac arrests;
- Computer-aided detection and diagnosis;
- Support for clinical decision making;
- Survival analysis;
- Drug discovery as an aid in therapy selection; and
- Analysis of electronic health records.
After the many triumphs of AI’s practical application in various industries, deep learning and artificial neural networks are now making great strides in dealing with various challenges in diagnostic imaging. Through deep learning, AI is finally making a significant impact on MRI reconstruction as well as other medical imaging applications.
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