Healthcare IT solutions have revolutionised modern healthcare. Take for example medical imaging – every year millions of patients undergo ultrasounds, MRIs and EX-Rays safely. These procedures create images that form the pivotal pillar of diagnosis. Doctors use the images to make decisions about illnesses and diseases of every kind.
Brief History And Definition Of Medical Imaging
In basic terms, medical imaging is the usage of physics application and some biochemistry to get a visual representation of the anatomy and biology of a living thing. It is believed that the first X-Ray was taken around 1895. Since then, we have progressed from blurry pictures that can hardly help medical professionals in making decisions to being capable of calculating 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 gone a paradigm shift. But not all technological advancements are able to translate to daily clinical practices. We take one such improvement – image analysis technology – and explain how it can be utilised in getting more data from medical images.
What is Image Analysis Technology?
When a computer is employed to study a medical image, it is known as image analysis technology. They are popular because a computer system is not handicapped by the biases of a human such as optical illusions and previous experience. When a computer examines an image, it doesn’t see it as a visual component. The picture is translated to digital information where every pixel of it is equivalent to a biophysical property.
The computer system uses an algorithm or program to find set patterns in the image and then diagnose the condition. The entire procedure is lengthy and not always accurate because the one feature across the picture doesn’t necessarily signify the same disease every time.
Using Machine Learning To Advance Image Analysis
A unique strategy for solving this issue related to medical imaging is machine learning. Machine learning is a kind of artificial intelligence that gives a computer to skill to learn from provided data without being overtly programmed. In other words: A machine is given different types of x-rays and MRIs
- It finds the correct patterns in them
- Then it learns to note the ones that have medical importance
The more data the computer is provided, the better its machine learning algorithm becomes. Fortunately, in the world of healthcare there is no shortage of medical images. Utilising them can make it possible to put into application image analysis at a general level. To further comprehend how machine learning and image analysis are going to transform healthcare practices, let’s take a look at two examples.
- Example 1:
Imagine an individual goes to a trained radiologist with their medical images. That radiologist has never encountered a rare disease that the individual has. The chances of the medical practitioners correctly diagnosing it are a bare minimum. Now, if the radiologist had access to machine learning the rare condition could be identified easily. The reason for it is that the image analysing algorithm could connect to pictures from all over the world and then develop a program that spots the condition.
- Example 2:
Another real-life application of AI-based image analysis is the measuring the effect of chemotherapy. Right now, a medical professional has to compare a patient’s images to those of others to find out if the therapy has given positive results. This is a time-consuming process. On the other hand, machine learning can tell in a matter of seconds if the cancer treatment has been effective by calculating the size of cancerous lesions. It can also compare the patterns within them with those of a baseline and then provide results.
The day when medical image analysis technology is as typical as Amazon recommending you which item to purchase next based on your buying history is not far. The benefits of it are not only lifesaving but extremely economical too. With every patient data we add on to image analysis programs, the algorithm becomes faster and more precise.
Not All Is Rosy
There is no denying that the benefits of machine learning in image analysis are numerous, but there are some difficulties too. A few obstacles that need to be crossed before it can see widespread use are:
- The patterns that a computer sees might not be understood by humans.
- The selection process of algorithms is at a nascent stage. It is still unclear on 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 are there any legal ramifications of it?
- What happens is the algorithm misses a tumour, or it 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 abnormalities that the algorithm identified, even if there is no treatment required for them?
A solution to all these questions needs to be found before the technology can be appropriated in real -life.