Healthcare IT solutions have revolutionized modern healthcare. Take medical imaging, for example: each year millions of patients safely undergo ultrasounds, MRIs and EX-rays. These procedures create images that form the central pillar of diagnosis. Doctors use images to make decisions about diseases and conditions of all kinds.
Brief history and definition of medical imaging
In basic terms, medical imaging is the use of applications of 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 because 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 such enhancement, image analysis technology, and explain how it can be used to gain 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 affected by the biases of a human being, such as optical illusions and previous experience. When a computer looks at 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 medical imaging problem 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, in the world of health there is no shortage of medical images. Its use can enable the application of image analysis at a general level. To better understand how machine learning and image analysis are going 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 all over 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 a patient’s images with those of others to see if the therapy has produced positive results. This is a process that requires a lot of time. On the other hand, machine learning can tell within seconds whether cancer treatment has been effective by calculating the size of cancerous lesions. You can also compare the patterns within them to those in a baseline, and then provide results.
The day is not far off when medical image analysis technology will be as commonplace as Amazon recommending which item to buy next based on your purchase history. The benefits of this are not only lifesaving, but also extremely economical. 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 in an incipient stage. It is not yet clear what is to be considered essential and what is not.
- How safe is it to use a machine to diagnose?
- Is using machine learning ethical 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 physician to inform the patient of all abnormalities identified by the algorithm, 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.