Medical IT solutions have revolutionized modern healthcare. Take, for example, medical imaging – every year, millions of patients who safely undergo ultrasound, MRI and EX. These procedures create images that form the main diagnostic support. Doctors use images to make decisions about diseases and illnesses of any kind.
Brief history and definition of medical imaging
In basic terms, medical imaging is the use of an application of physics and some biochemistry to get a visual representation of the anatomy and biology of a living being. It is believed that the first x-ray was taken around 1895. Since then, we have received blurry images that can hardly help medical professionals in making decisions that allow us to calculate the effect of oxygenation on the brain.
At present, understanding of the disease that destroys the human body has increased exponentially, because in the field of medical imaging there has been a paradigm shift. But not all technological advances can lead to daily clinical practice. We take one of these improvements — an image analysis technology — and explain how it can be used to get more data from medical images.
What is image analysis technology?
When a computer is used to study medical images, it is called image analysis technology. They are popular because the computer system is not disconnected due to human prejudices, such as optical illusions and previous experience. When the computer checks the image, it does not see it as a visual component. The image is translated into digital information, where each pixel is equivalent to the biophysical property.
A computer system uses an algorithm or program to find installed patterns in an image and then diagnose a condition. The whole procedure is long and not always accurate, because there is no need to designate the same disease each time.
Using machine learning for advanced image analysis
A unique strategy to solve this problem associated with medical imaging is machine learning. Machine learning is a kind of artificial intelligence that allows a computer to learn from the data provided without explicit programming. In other words: the machine gives various types of X-rays and MRI.
He finds the right patterns in them.
He then learns to mark those that have medical value.
The more data is provided to a computer, the better its machine learning algorithm becomes. Fortunately, there is no shortage of medical images in the world of healthcare. Their use may allow for the analysis of application images at a general level. To understand how teaching and image analysis will transform health care practice, let's look at two examples.
- Example 1:
Imagine someone going to an experienced radiologist with their medical images. This radiologist has never encountered a rare disease that a person has. The chances of doctors to correctly diagnose is the minimum. Now, if the radiologist had access to machine learning, it would be easy to identify a rare condition. The reason for this is that the image analysis algorithm can be associated with images from around the world, and then develop a program that defines the state.
- Example 2:
Another real-world application of image analysis based on AI is to measure the effect of chemotherapy. Right now, a professional doctor should compare the images of the patient with the images of other people to find out if the therapy has yielded positive results. This is a time consuming process. On the other hand, machine learning can determine in a matter of seconds how effective cancer treatment is by calculating the size of cancer lesions. He can also compare the patterns inside them with the baseline patterns and then provide the results.
Day when medical image analysis technology This is just as typical as Amazon recommends that you buy the next product based on your purchase history close by. The advantages of this not only save lives, but also extremely economical. With each patient information we add to image analysis programs, the algorithm becomes faster and more accurate.
Not all pink
There is no denying that the advantages of machine learning in image analysis are numerous, but there are some difficulties. Here are a few obstacles that need to be overcome before it sees widespread use:
Patterns that a computer sees may not be understood by people.
The process of selecting algorithms is at the initial stage. It is still unclear what should be considered necessary and what is not.
How to use the machine for diagnostics?
Is it ethical to use machine learning and does it have any legal implications?
What happens if the algorithm misses a tumor or incorrectly determines the state? Who is held responsible for the error?
Is the doctor obliged to inform the patient about all deviations detected by the algorithm, even if they do not require any treatment?
It is necessary to find a solution to all these issues before the technology is adapted in real life.