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Healthcare IT solutions have revolutionized modern healthcare. Take medical imaging, for example – every year millions of patients undergo ultrasound, MRI and X-rays safely. These procedures create images that form the central pillar of diagnosis. Doctors use pictures to make decisions about diseases and diseases of every kind.
A brief history and definition of medical imaging
In basic terms, medical imaging is the use of the application of physics and some biochemistry to obtain a visual representation of the anatomy and biology of a living thing. The first X-rays are believed to have been taken around the year 1895. Since then, we have progressed from blurry images that can hardly help medical professionals make decisions so they can compute the effects of oxygen on the brain.
Nowadays, the understanding of diseases afflicting the human body has increased exponentially because the field of medical imaging has made a qualitative shift. However, not all technological developments are able to translate into everyday clinical practices. We take one of these improvements – Image Analysis Technology – and show how it can be used to get more data from medical images.
What is image analysis technology?
When using a computer to study a medical image, it is known as image analysis technology. It's popular given that the computer system is not hindered by human biases such as optical illusions and past experience. When the computer scans an image, it does not see it as a visual component. The image is translated into digital information, with each pixel equivalent to a biophysical property.
The computer system uses an algorithm or program to find specific patterns in the image and then diagnose the condition. The whole procedure is long and not always accurate because the only feature in the image does not necessarily mean the same disease every time.
Use machine learning to enhance image analysis
The unique strategy for solving this medical imaging problem is machine learning. Machine learning is a type of artificial intelligence that gives a computer the skill to learn from the data provided without being programmed publicly. In other words: the machine is given different types of X-ray and MRI
- He finds the right patterns in it
- Then he learns to note which are of medicinal importance
The more data that is provided by the computer, the better the machine learning algorithm becomes. Fortunately, there is no shortage of medical images in the world of healthcare. Utilizing them can make it possible to put them into application picture analysis at a global level. For more understanding on how machine learning and image analysis transform health care practices, let's take a look at two examples.
- Example 1:
Imagine that the individual goes to a trained radiologist with their medical pictures. This radiologist did not come across a rare disease that affected an individual. The chances of physicians practicing to diagnose it correctly is minimal. Now, if the radiologist has access to machine learning, the rare condition can be easily identified. The reason for this is that the image analysis algorithm can communicate with images from all over the world and then develop a program that determines the state.
- Example 2:
Another real-life application of artificial intelligence-based image analysis is measuring the effect of chemotherapy. Currently, the medical professional must compare the patient's photos with the pictures of others to see if the treatment has produced positive results. This is a time consuming process. On the other hand, machine learning can know within seconds whether cancer treatment is effective by calculating the size of cancerous lesions. The patterns within them can also be compared to baseline styles and then present results.
The day medical imaging technology is as typical as Amazon recommended for the item that you should buy next based on your purchase history, is not far away. The benefits of this are not only life-saving but also very economic. With every patient data we add to our image analysis software, 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 some difficulties as well. Some obstacles to be overcome before you see widespread use are:
- Humans may not understand the patterns seen by the computer.
- The algorithm selection process is in its early stages. It is still not clear what should be considered necessary or not.
- How safe is it to use a device to diagnose the disease?
- Is it ethical to use machine learning and are there any legal implications for it?
- What happens is that the algorithm is missing a tumor or is it incorrectly determining the condition? Who is responsible for the error?
- Is it the doctor's duty to inform the patient of all the defects identified by the algorithm, even if no treatment is required for them?
All of these questions must be solved before technology can be customized in real life.
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