Healthcare IT solutions have revolutionized modern healthcare. Take medical imaging, for example: Every year, millions of patients safely undergo ultrasounds, MRIs, and EX-rays. These procedures create images that form the mainstay of diagnosis. Doctors use images to make decisions about diseases and conditions of all kinds.
Brief history and definition of medical images
In basic terms, medical imaging is the use of applications from physics and some biochemistry to obtain a visual representation of the anatomy and biology of a living thing. The first X-ray is believed to have been taken around 1895. Since then, we have gone from blurry images that can hardly help medical professionals in decision making to being able to calculate the effects of oxygenation on the brain.
Currently, the understanding of the diseases that plague the human body has increased exponentially due to the fact that the field of medical imaging has undergone a paradigm shift. But not all technological advances can be translated into daily clinical practices. We take one of those enhancements, image analysis technology, and explain how it can be used to get more medical imaging data.
What is image analysis technology?
When a computer is used to study a medical image, it is known as image analysis technology. They are popular because a computer system is not harmed by the biases of a human being, such as optical illusions and prior experience. When a computer examines an image, it doesn’t see it as a visual component. The image is translated into digital information where each pixel is equivalent to a biophysical property.
The computer system uses an algorithm or program to find established patterns in the image and then diagnose the condition. The entire procedure is lengthy and not always accurate because the single feature that appears on the image does not necessarily mean the same disease each time.
Using machine learning to advance image analysis
A unique strategy to solve this problem related to medical images is machine learning. Machine learning is a type of artificial intelligence that gives a computer the ability to learn from given data without being overtly programmed. In other words: a machine receives different types of X-rays and MRIs
- Find the correct patterns in them.
- Then learn to write down the ones that are medically important.
The more data that is fed to the computer, the better its machine learning algorithm will be. Fortunately, medical imaging is not lacking in the world of health. Its use can enable the application of image analysis at a general level. To better understand how machine learning and image analytics are set to transform healthcare practices, let’s take a look at two examples.
- Example 1:
Imagine that a person goes to a trained radiologist with their medical images. That radiologist has never come across a rare disease that the individual has. The chances of doctors diagnosing it correctly are slim. Now, if the radiologist had access to machine learning, the rare condition could be easily identified. The reason for this is that the image analysis algorithm could connect to images from around the world and then develop a program that detects the condition.
- Example 2:
Another real-life application of AI-based image analysis is the measurement of the effect of chemotherapy. Right now, a medical professional has to compare the images of one patient with those of others to know if the therapy has given positive results. This is a very time consuming process. On the other hand, machine learning can tell in a matter of seconds whether cancer treatment has been effective by calculating the size of cancerous lesions. You can also compare the patterns within them to those of a baseline and then provide results.
The day is not far off when medical image analysis technology is as commonplace as Amazon recommending you which item to buy next based on your purchase history. The benefits of this are not only lifesaving, but also extremely inexpensive. With each patient data we add to image analysis programs, the algorithm becomes faster and more accurate.
Not everything is rosy
There is no denying that the benefits of machine learning in image analysis are numerous, but there are also some pitfalls. Some hurdles that need to be crossed before you can see widespread use are:
- Humans may not understand the patterns a computer sees.
- The algorithm selection process is at an incipient stage. It is not yet clear what should be considered essential and what not.
- How safe is it to use a machine to diagnose?
- Is it ethical to use machine learning and does it have any legal ramifications?
- What happens if the algorithm misses a tumor or incorrectly identifies a condition? Who is considered responsible for the error?
- Is it the duty of the doctor to inform the patient of all the anomalies that the algorithm identifies, even if they do not require treatment for them?
A solution to all these questions needs to be found before the technology can be appropriated in real life.
