Evaluating brain MRI scans with the help of artificial intelligence

Greece is just one example of a population where the share of the elderly is expanding, and with it the incidence of neurodegenerative diseases. Of these, Alzheimer’s disease is the most prevalent, accounting for 70% of cases of neurodegenerative disease in Greece. An estimated 197,000 people currently suffer from the disease, according to estimates published by the Alzheimer’s Society of Greece. This number is expected to increase to 354,000 by 2050.

Dr. Andres Papadopoulos1, A leading diagnostic provider near Athens, Greece, physician and scientific coordinator of Atropolis Medical Group, explains the key role of early diagnosis: But then it doubles. Every five years. Existing drugs cannot reverse the course of degeneration; They can only slow it down. That’s why it’s important to make a proper diagnosis at an early stage જ્યારે when the first mild cognitive impairment appears અને and to filter out Alzheimer’s patients.2,

Diseases such as Alzheimer’s or other neurodegenerative pathology typically have a very slow progression, making it difficult to identify and quantify pathological changes on an early stage MRI image of the brain. When evaluating scans, some radiologists describe this process as one of “guesswork”, as it is not always possible to observe visual changes in the extremely complex anatomy of the brain with the human eye. This is where technological innovations, such as artificial intelligence, can provide support for the interpretation of clinical images.

One such tool is the AI-Rad Companion Brain MR3Part of a family of decision-making solutions for imaging, AI-Rad Companion Brain MR is brain volumetric software that provides automatic volumetric authentication of different parts of the brain. “It is able to separate them from each other: it separates the hippocampi and brain lobes and determines the amount of white matter and gray matter for each segment individually.” Says Dr. Papadopoulos. Altogether, it has the ability to divide, measure, and illuminate more than 40 areas of the brain.

Calculating volumetric properties manually can be a daunting and time consuming task. “More importantly, it also includes certain observations that humans simply cannot achieve.” Says Dr. Papadopoulos. Papadopoulos has always been an early adopter and has welcomed technological innovations in imaging throughout his career. This AI-powered tool means it can now even compare authentication with standard data from a healthy population. And it’s not all about automation: the software displays data in a structured report and creates a highlighted deviation map based on user settings. It also allows the user to manually monitor volumetric changes along with all the key data previously generated automatically.

Opportunities for more accurate observation and evaluation of volumetric changes in the brain encourage Papadopoulos while considering how important early detection of neurodegenerative diseases is. He explains: “In the early stages, volumetric changes are small. The hippocampus, for example, has a volume reduction of 10% to 15%, which is very difficult for the eye to detect. But the objective calculations provided by the system can be of great help.

The purpose of AI is to relieve physicians from significant burdens and, ultimately, to save time when best embedded in the workflow. An extremely valuable role for this particular AI-powered postprocessing tool is that it can visualize deviations of various structures that can be difficult to identify with the naked eye. Papadopoulos already recognizes that the biggest advantage in his work is the “AI-Red Companion Brain MR which provides the objective framework on which he can base his subjective assessment during the examination.”

AI-Red Companion4 Clinicians from Siemens Healthineers support their diagnostic decision-making routine. To maintain a consistent value stream, our AI-powered tools include regular software updates and upgrades that are submitted to customers through the cloud. Customers can decide whether they want to integrate a full cloud-based approach into their working environment taking advantage of all the benefits of the cloud or a hybrid approach that allows them to process imaging data in their own hospital IT setup.

The next software version of AI-Rad Companion Brain MR will have new algorithms capable of segmenting, validating and visualizing White Matter Hyperintency (WMH). Reporting WHM, along with McDonald’s criteria, assists in the assessment of multiple sclerosis (MS).

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