The bright future of healthcare with medical imaging software

IT solutions for healthcare have revolutionized modern healthcare. Take, for example, medical imaging – millions of patients safely undergo ultrasounds, MRIs, and EX rays each year. These procedures create images that form the cornerstone of diagnosis. Doctors use the 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 physical applications and some biochemistry to obtain a visual representation of the anatomy and biology of a living being. The first X-ray is believed to have been taken around 1895. Since then, we have gone from fuzzy images that can hardly help medical professionals make decisions to being able to calculate the effects of oxygenation on the brain.

Today, the understanding of the diseases that plague the human body has increased exponentially as 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 improvements, image analysis technology, and explain how it can be used to gain more data from medical images.

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 hampered by a human’s biases, such as optical illusions and prior experience. When a computer examines an image, it does not 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 whole procedure is long and not always accurate because the only feature in the picture does not necessarily mean the same disease every 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 provided data without being overtly programmed. In other words: a machine receives different types of X-rays and MRIs.

1. Find the correct patterns in them.

2. Then learn to notice the ones that are medically important.

The more data the computer provides, the better its machine learning algorithm will be. Fortunately, there is no shortage of medical imaging in the world of health. Its use can make it possible to apply image analysis at a general level. To better understand how machine learning and image analytics 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 encountered a rare disease that the individual has. The chances that doctors will diagnose 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. At this point, a medical professional has to compare the images of one patient with those of others to see if the therapy has produced positive results. This is a time consuming process. On the other hand, machine learning can tell within seconds whether cancer treatment has been effective by calculating the size of cancer lesions. You can also compare the patterns within them to those of a baseline and then provide results.

The day that medical imaging technology is as common as Amazon recommending which item to buy next based on your purchase history is not far off. Its benefits are not only life-saving, but extremely inexpensive as well. With each patient information 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 obstacles that must be crossed before widespread use can be seen 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 should and should not be considered essential.

• How safe is it to use a diagnostic machine?

• Is it ethical to use machine learning and are there legal ramifications of it?

• What if the algorithm doesn’t detect a tumor or misidentifies a condition? Who is held responsible for the error?

• Is it the doctor’s duty to inform the patient of all abnormalities that the algorithm identified, even if no treatment is required for them?

A solution to all these questions needs to be found before technology can take over in real life.