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 are the central pillar of diagnosis. Doctors use the images to make decisions about all kinds of diseases.
Brief history and definition of medical imaging
In general terms, medical imaging is the use of physics applications and some biochemistry to provide a visual representation of the anatomy and biology of a living being. It is believed that the first radiograph was taken around 1895. Since then, we’ve moved from blurry photos that can barely help medical professionals make decisions about how to calculate the effects of oxygenation in the brain.
At present, the understanding of the diseases that plague a human body has increased exponentially as the field of medical imaging has undergone a paradigm shift. However, not all technological developments can be translated into daily clinical practices. We take such an enhancement – image analysis technology – and explain how it can be used to extract 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 human prejudices, such as optical illusions and past experiences. When a computer examines an image, it doesn’t view 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 fixed patterns in the image and then diagnose the condition. The whole procedure is long and not always accurate, because the only feature in the photo does not necessarily mean the same disease every time.
Use Machine Learning to promote 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 overly programmed. In other words, a machine receives different types of X-rays and MRIs
It finds the right patterns in it
Then it learns to notice those who are medically important
The more data the computer provides, the better the machine learning algorithm becomes. Fortunately, in the world of healthcare, there is no shortage of medical images. By using them, it may become possible to apply a general image analysis. Let’s look at two examples to further understand how machine learning and image analysis are going to transform healthcare practices.
- Example 1:
Imagine someone going to a trained radiologist with their medical images. That radiologist has never encountered a rare disease that the individual has. The chances of the doctor diagnosing it correctly are an absolute minimum. If the radiologist now 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 to measure the effect of chemotherapy. At this point, a medical professional should compare a patient’s images to others’ images to find out if the therapy has had positive results. This is a time consuming process. On the other hand, machine learning can see in seconds whether the cancer treatment has been effective by calculating the size of cancerous lesions. It can also compare the patterns therein with those of a baseline and then produce results.
The day when medical image analysis technology is just as typical as Amazon that you recommend which item to buy next based on your buying history is not far away. Its benefits are not only life-saving, but also extremely economical. With every patient data we add to image analysis programs, the algorithm becomes faster and more accurate.
Not everything is rosy
It is undeniable that the benefits of machine learning in image analysis are numerous, but there are also some problems. A few obstacles that must be crossed before it can be widely used are:
The patterns that a computer sees may not be understood by people.
The algorithm selection process is at a nascent stage. It is still unclear what should be considered essential and what not.
How safe is it to use a machine to make a diagnosis?
Is it ethical to use machine learning and are there legal consequences for it?
What happens if the algorithm misses a tumor or misidentifies a condition? Who is believed responsible for the error?
Is it the physician’s duty to notify the patient of any abnormalities identified by the algorithm even if no treatment is required?
All these questions must be resolved before technology can be appropriated in real life.