Healthcare IT solutions have revolutionized modern healthcare. Take medical imaging, for example – millions of patients have safe ultrasounds, MRIs and EX rays every year. These procedures create images that form the central pillar of diagnosis. Doctors use the pictures to make decisions about illnesses and diseases of all kinds.
Brief history and definition of medical imaging
In basic terms, medical imaging is the use of the application of physics and 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 blurry images that can hardly help healthcare professionals make decisions to being able to calculate the effects of oxygenation in the brain.
At present, the understanding of the diseases that ravage a human body has been increased exponentially because the field of medical imaging has changed paradigm. But not all technological advances are capable of translating into everyday clinical practice. We take one of those improvements – 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 a medical image, it is referred to as image analysis technology. They are popular because a computer system is not crippled 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, each pixel of which is equivalent to a biophysical property.
The computer system uses an algorithm or program to find defined patterns in the image and then diagnose the condition. The whole procedure is long and not always precise because the only feature in the picture does not necessarily mean the same disease every time.
Using Machine Learning to Advance Image Analysis
Machine learning is a unique strategy for solving this medical imaging problem. Machine learning is a kind of artificial intelligence that gives a computer the skills to learn from the data provided without being overtly programmed. In other words: a machine receives different types of x-rays and MRIs
He finds the right models in them
Then he learns to note those that are of medical importance.
The more data the computer provides, the better its machine learning algorithm. Fortunately, in the world of health, there is no shortage of medical images. Their use can make it possible to implement image analysis at a general level. To better understand how machine learning and image analysis will transform healthcare practices, let’s look at two examples.
- Example 1:
Imagine that an individual goes to a qualified radiologist with his medical images. This radiologist has never encountered a rare disease in the individual. The chances that doctors will diagnose it correctly are a bare minimum. Now, if the radiologist had access to machine learning, the rare disease could be easily identified. The reason is that the image analysis algorithm could connect to images around the world and then develop a program that detects the condition.
- Example 2:
Another real application of AI-based image analysis is in measuring the effect of chemotherapy. Currently, a healthcare professional must compare one patient’s images with those of others to see if therapy has been successful. It is a process that takes time. On the other hand, machine learning can tell in seconds whether cancer treatment has been effective by calculating the size of cancerous lesions. It can also compare the models they contain with those in a baseline and then provide results.
The day when medical image analysis technology is also typical that Amazon recommends which item to buy next based on your purchase history is not far off. The advantages of it are not only life saving, but also extremely economical. With each patient data that we add to image analysis programs, the algorithm becomes faster and more precise.
All is not rosy
There is no denying that the benefits of machine learning in image analysis are many, but there are also challenges. Some hurdles to overcome before seeing widespread use are:
The patterns that a computer sees may not be understood by humans.
The algorithm selection process is in its early stages. It is still not clear what should be considered essential and what is not.
Is it safe to use a machine to diagnose?
Is it ethical to use machine learning and are there legal ramifications?
What if the algorithm misses a tumor or incorrectly identifies a condition? Who is considered responsible for the error?
Does the doctor have a duty to inform the patient of all the abnormalities identified by the algorithm, even if no treatment is required for them?
A solution to all of these questions must be found before the technology can be appropriate in real life.
Source by Uma Nathan