Blog post

Using AI in Medical Imaging to Augment Radiologists’ Efforts

Learn more about the potential of artificial intelligence in medical imaging practices

August 28, 2020

7 mins read

Having evolved from basic 2D pictures, today’s tomographic images fascinate with high anatomical detail, making medical imaging more insightful than ever. But the increased amount of data to be processed has led to complications.

With images becoming more data-rich, radiologists are forced to focus on image analysis and reporting. As a result, they must leave interpretation to non-radiologists, which can negatively influence health outcomes. Budgetary constraints and an aging population coupled with the time-consuming process of image analysis is probably the reason for the catastrophic shortage of radiologists across Europe. The UK has the lowest number of trained medical imaging experts per capita — 4.7 radiologists per 100,000 people instead of the 8 radiologists needed.

But with so many demands placed on radiologists, hiring more will not suffice. Integrating technological enhancements like artificial intelligence is necessary. In this article, you’ll learn more about the potential of AI in medical imaging and its possible applications.

The potential of AI

AI-based radiology software uses machine learning algorithms, big data, and computer vision for medical imaging to view and interpret MRIs, CT scans, CAT scans, and other images, augmenting a radiologist’s performance or sometimes functioning as a standalone tool.

The fundamental steps of classic machine learning and computer vision techniques in medical imaging include:

  • Image acquisition and pre-processing — producing a digital image with image sensors
  • Image segmentation — partitioning the digital image into segments, locating objects and boundaries, and categorizing the entire image into a class, such as “brain” or “lungs”
  • Feature selection and extraction —identifying the most discriminant features
  • Classification — analyzing the most discriminant features

For example, say we want to recognize a type of plant. First, we determine that an image is of a plant (image acquisition). Then we decide that one part is a stem and the other part is a leaf (segmentation). After that, we determine that the length of the stem and the color of the leaf are the most important features for identification (feature selection and extraction). Finally, after analyzing these two features, we decide that the plant is an eggplant, not a tomato (classification).

Applying artificial intelligence to image processing in the medical field has the immense potential to simplify and accelerate the entire radiological workflow while significantly reducing the rate of missed diagnoses and improving health outcomes. Below are a few characteristics of AI in radiology and practical values it already brings or might bring in the future.

Ability to process large volumes of data

AI-driven medical imaging solutions rely heavily on large volumes of medical data to train their machine learning algorithms to detect abnormal scans. When interpreting scans, AI-based solutions not only compare the scans with cases they’ve learned but also consider additional medical information, including previous scans of the same patient. This allows an AI solution to make diagnoses based on context, boosting precision manifold. Additionally, data obtained from previous scans can be uploaded into an electronic health record system for future reference.

Increased sensitivity to hidden abnormalities

AI-powered computer-aided diagnosis technologies can find abnormalities that the most experienced and detail-oriented radiologist is unable to catch. By giving radiologists consistent and reliable data, artificial intelligence in medical imaging can minimize subjectivity and variations in interpretations between radiologists. In this way, it can improve the reproducibility of radiological results.

Context-based interpretation

Using the power of computer vision in healthcare applications, AI systems can interpret images in context, making them similar to human radiologists. Since not all abnormalities are necessarily representative of a disease, ML algorithms in AI-based CAD solutions are trained on a case-by-case basis to see features invisible to the human eye. This makes them a decent alternative to conventional CAD systems, which just detect the absence or presence of visible features.

The characteristics mentioned above suggest that artificial intelligence can automate many routine tasks (such as detecting, characterizing, and quantifying abnormalities) as well as more complex tasks (such as interpreting findings within the clinical context). This could tackle the workforce crisis in medical imaging we’re witnessing in 2020.

What practical improvements does AI bring?

Though AI-based medical image processing is only in its infancy, it has already brought certain practical improvements to the medical imaging field. In a study published in European Radiology Experimental, Filippo Pesapane, Marina Codari, and Francesco Sardanelli point out that AI can:

  • perform risk stratification and quickly identify cases without abnormalities, allowing radiologists to prioritize and dedicate time to patients who really need treatment while not prescribing unnecessary medication
  • compare current examinations against previous ones to track disease progress
  • aggregate medical information to interpret scans with the full context
  • facilitate the peer review of medical imaging reports
  • improve technicians’ performance
  • ensure effective communication between technicians, radiologists, and other experts involved in medical image processing.

As shown above, artificial intelligence can augment every stage of the medical imaging chain. But what about particular medical imaging areas? Let’s unpack this below.

Key segments in AI-based medical imaging

There’s no doubt that AI has the immense potential to revolutionize healthcare as a whole. But when it comes to medical imaging in particular, breast imaging, cardiovascular imaging, lung imaging, and neurological imaging are the key areas to benefit from artificial intelligence. Let’s see how.

Using AI in Medical Imaging to Augment Radiologists’ Efforts

Lung imaging

Producing high-resolution images of the lungs, AI-powered technology provides better quality of lung screening. Lung cancer is the most diagnosed form of cancer in many countries, and some advancements have been made in its early detection and treatment.

For example, an AI-based image evaluation solution developed by Google and Northwestern University Feinberg School of Medicine uses deep learning to detect malignant lung nodules on CT scans. While human radiologists normally view numerous 2D lung images, this solution examines lungs in a single huge 3D image, making the screening output more accurate. Besides, the system is trained to compare both primary and prior CT scans, which helps predict the risks of lung cancer malignancy. It analyzes both the region of interest and that with a high likelihood of lung cancer. This solution shows the same performance as expert radiologists when both primary and prior CT scans are available and outperforms them when a prior scan isn’t available.

Breast imaging

Artificial intelligence can tackle the main issues breast imagers face — missed cancers, false positives, and an ever-increasing workload. When combined with other innovative techniques such as digital breast tomosynthesis (3D mammography), which provides a remarkable level of detail, AI shows decent performance in the early detection of breast cancer. Moreover, in a recent study, a group of radiologists showed better performance when using an AI algorithm as a decision support tool than when working on their own. Interestingly, when used as a standalone tool, the same algorithm demonstrated the average diagnostic performance of a human radiologist.

Another study aimed to reveal the risk stratification capabilities of AI-based algorithms and their potential to reduce a radiologist’s workload. In the study, the risk measurement system was trained to rank mammography examinations from 1 to 10 based on the likelihood of cancer presence. While scores from 1 to 5 were automatically considered normal, radiologists were assigned to examine cases scored 6 to 10. As a result, the overall workload was reduced by 47% with a 7% rate of missed cancer, which is very promising.

How to build an AI-driven mammography solution and what results it may bring?

Brain (neurological) imaging

AI-driven algorithms in brain imaging help to detect abnormalities that are otherwise invisible. This is especially important for medication-resistant epilepsy patients for whom surgery is the only way to get rid of seizures. But the problem is that one-third of these patients have normal-looking MRI brain scans. And without locating seizure focal points, it’s impossible to perform surgery. However, a group of Canadian epilepsy experts seems to have tackled this problem with the help of machine learning.

In their experiment, the researchers used machine learning to train computers to read MRI scans that don’t reveal any visible abnormalities. The results were astonishing: the method allowed them to locate the focal point of seizures for a 27-year-old woman with normal-looking MRI scans. The woman underwent surgery and is now free of seizures.

Using AI in Medical Imaging to Augment Radiologists’ Efforts

Cardiovascular imaging

Artificial intelligence can be utilized in many steps of cardiovascular imaging, including image acquisition, segmentation, interpretation, and prediction of patient outcomes. Besides that, ML-driven algorithms are expected to become an integral part of standard cardiac imaging reporting and quantitative analysis software, gathering data and automatically performing risk stratification.

Certain solutions have already been implemented. HeartVista, developed by RTHawk, delivers detailed images quicker than any of its conventional counterparts. LVivo EF cardiac decision support software is the first AI-powered pocket-sized ultrasound that uses advanced pattern identification algorithms. Arterys is capable of partially automating a complex cardiac analysis.

Conclusion

The time-consuming process of analyzing images, complicated reporting routines, high rates of missed diagnoses, budgetary constraints, and a lack of trained radiologists have forced healthcare providers to turn their attention to artificial intelligence. AI in healthcare — and in medical imaging in particular — is only in its early development phase. However, compelling evidence of its effectiveness has already been revealed across the field’s key domains.


If you’re looking for a reliable software partner who specializes in building AI-based solutions for medical imaging, you’re in the right place. Contact our experts to learn more about applying artificial intelligence and advanced tech to optimize care pathways and improve treatment outcomes.

Your subscription is confirmed.
Thank you for being with us.

No votes Thank you for your vote. 26896 609f02512a

Tell us about your project

I give consent to the processing of my personal data given in the contact form above under the terms and conditions of Intellias Privacy Policy. I want to receive commercial communications and marketing information from Intellias by electronic means of communication (including telephone and e-mail).
* I give consent to the processing of my personal data given in the contact form above under the terms and conditions of Intellias Privacy Policy.

Awards and recognition

logo
logo
logo
logo
logo
logo

Thank you for your message.
We will get back to you shortly.

Thank you for your message.
We will get back to you shortly.