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How Medical Image Analysis Can Impact Healthcare


Nov 4, 2020 - 7 minute read

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Rafał Imielski Content Marketing Specialist

He has two years’ experience in copywriting, translation and proofreading. His goal is to help people communicate in a concise and understandable way. Rafał is an archaeology graduate who’s fascinated by both prehistoric and modern technologies. 

See all Rafał's posts

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Medical specialists around the world are flooded with all kinds of data that they have to collect, process, and analyse. As people, they have a limited capacity, and they’re susceptible to fatigue that’s detrimental to both their own health and their ability to help their patients. Medical images make up around 90% of the data in healthcare. The demand for medical imaging is growing, which results in an increasing amount of data that needs to be processed.

This fact opens up the possibility to use innovative IT solutions and employ medical image analysis software. Artificial Intelligence solutions can reduce the time patients wait to receive their diagnoses, expand the number of people that can be examined, increase diagnostic accuracy, and help healthcare organisations optimise their operations.

Machine Learning in Image Analysis

Machine Learning lies at the heart of image analysis technology. No matter which area of medicine (or other industries) we consider, the software has to be properly trained in order to recognise specific anomalies. For AI, the learning process is significantly different than for people. It utilises extremely large image datasets that have to be processed and analysed by the software. The AI solution then detects significant patterns and uses this knowledge to recognise signs of specific medical conditions which can be observed in these images.

It’s important to note that solutions based on Machine Learning can and, in several cases, already have surpassed human doctors in terms of diagnostic accuracy. Of course, a certain level of supervision by trained specialists is always required. The software can struggle at times, especially with images that are flawed, incomplete or simply poor quality. That’s when the experience of a human doctor and the ability to look at an image without converting it to ones and zeros might be necessary.

Medical Image Analysis in Different Areas of Healthcare

Various medical imaging technologies have played an important role in healthcare for many years. Wilhelm Röntgen discovered the x-ray in the late 19th century and this technology started to be used in medicine shortly after. Today, multiple types of machines and devices are frequently used to diagnose patients by generating images of body parts, often internal organs.

IT solutions already are a popular tool for managing this type of data—it’s difficult to imagine a world where storing, processing, and manipulating of all the medical images happens without any digital involvement. Technologies can prove to be even more useful if they’re also used to analyse this data.

Image Analysis Solutions in Medicine

Medical image analysis solutions are relevant to many areas of healthcare, including but not limited to neurology, cardiology, orthopaedics, dentistry, and oncology. They can read images produced with the use of an X-ray, MRI, CT, PET, and ultrasounds. As a result, there’s an opportunity for automation and acceleration of tasks that require a long time when performed manually. The software can learn to recognise specific features of these images, thus making the diagnostic process significantly faster and more reliable.

The possibilities of medical image processing software are impressive. AI-based solutions not only detect irregularities and identify potentially dangerous anomalies, but they’re also able to determine whether they’re dealing with cancer or just a benign tumour. In addition to analysing traditional 2D images, the software can also read 3D and so-called 4D images (ones that display changes in time). Obviously, there’s no need to rely purely on AI in the diagnostic process—it can be used to filter out the healthy patients and alert the doctors when their attention is required. The goal is not to replace physicians and other medical professionals, but to take a part of the burden off them and enable them to help more patients.


The medical use of X-rays comes down to producing images of the patient’s internal body parts. Some of the most popular conditions that can be detected with this technology include lung diseases and bone fractures. It’s the oldest form of medical imaging, and it’s still frequently used. In 2015, over 22 million X-ray examinations have been performed on NHS patients alone. At the same time, there weren’t enough radiologists to quickly analyse all these images, and some patients had to wait more than a month for their diagnoses.

Image analysis software also has its uses in mammography. This medical technique uses X-rays to detect breast cancer. Research in South Korea showed that, with the aid of trained AI models, doctors can identify the disease in cases where it would otherwise be impossible.

Considering the above, IT solutions can go a long way to not only help specialists deal with large volumes of data, but also increase the accuracy of their diagnoses. Moreover, similarly to the ultrasound, X-ray examinations are quite common in developing countries, where there is often a limited number of doctors. Image analysis software can provide the radiologists with much-needed support.

CT Scanning

Computed Tomography (CT) is often used to detect tumours, anomalies in blood vessels, abscesses, and multiple other medical conditions. It can produce 2D slices of the patient’s body or even 3D images. This type of patient examination has become significantly more popular in recent years. The large number of produced images makes it possible to assemble extensive training data sets.

At the same time, this presents an opportunity for IT to take over some of the workload and make sure that patients receive their diagnoses faster. Moreover, as shown by researchers at the University of Central Florida, medical image analysis software can very accurately detect specific diseases. In this specific case, scientists created an efficient system that detected small lung tumours which are difficult to notice for radiologists.

PET Scanning

Positron Emission Tomography (PET) is a medical imaging technique to detect cancer, blood flow issues, brain pathologies, and bone formation anomalies. It utilises special radioactive substances called tracers. This substance is injected into the patient’s body and then a special scanner is used to produce the images. As a result, medical professionals can observe the tracer inside the patient’s body and use this information to make a diagnosis.

Unfortunately, the biggest drawback of this method is the exposure of patients to the radioactive tracers. IT solutions can, at least partially, alleviate this issue. With proper training datasets, it’s possible to develop software which will improve the quality of produced images and remove image noise. This may reduce the overall involvement of radioactive substances. At the same time, there’s the possibility of developing software that’s capable of analysing PET images and identifying symptoms of specific diseases.


Magnetic Resonance Imaging utilises magnetic fields and radio waves to produce images of internal body parts. It’s mostly used to detect soft tissue conditions, such as aneurysms and circulatory system issues. Unlike the techniques discussed earlier, the MRI doesn’t expose the patient to any radiation. In 2016, almost 40 million MRI scans were performed in the United States alone.

Once again, Artificial Intelligence can be used to analyse these images and detect diseases. The researchers at the Osaka City University managed to develop a deep learning solution that allowed for accurate automated detection of cerebral aneurysms based on MR images. This is another area where the use of AI can not only reduce the workload of radiologists but also improve the reliability of their diagnoses.

Furthermore, AI solutions can be used to detect the early stages of Alzheimer’s disease. Researchers at the University of California used historical patient data to train and test a model that analyses brain scans and recognises early signs of Alzheimer’s. The software was able to detect the condition by looking at scans that were taken six years before the patients were diagnosed with the disease.


Ultrasound imaging is a technology that’s used across multiple industries. In medicine, it can be utilised to produce images and videos of internal organs, muscles, tendons, and blood vessels. It’s also used to track the development of a foetus during pregnancy.

Medical ultrasounds are a common and non-invasive form of examination. The sheer amount of produced data makes it a great area for medical image analysis software. Reading and interpreting these images is a time-consuming task for radiologists. With the use of Machine Learning, AI solutions can take over a large portion of this workload. As shown in the 2018 NCBI research, deep learning software is able to use ultrasound images to identify breast cancer with accuracy that’s comparable to radiologists. This is especially relevant in developing countries that struggle with a shortage of doctors.


Medical images make up for a vast majority of data that needs to be processed and analysed in the healthcare industry. The number of images generated worldwide increases every year and this trend is unlikely to change, as more and more people will gain access to better medical care. Radiologists are struggling to keep up with large volumes of data, aseven in the wealthiest countries, their workforce is growing significantly slower than the demand for such services.  

This is where digital solutions can come to the rescue. Using Machine Learning algorithms and large training datasets, medical image analysis software is able to accurately recognissymptoms of specific conditions. These types of diagnostic tools are can take over some of the more time-consuming tasks and enable doctors to focus on problems that require their direct attention. As such, the smart use of AI solutions can help healthcare organisations provide better and more time-efficient care.

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Rafał Imielski Content Marketing Specialist

He has two years’ experience in copywriting, translation and proofreading. His goal is to help people communicate in a concise and understandable way. Rafał is an archaeology graduate who’s fascinated by both prehistoric and modern technologies. 

See all Rafał's posts

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