Medical imaging generates a lot of data, for example during computer tomography. This data is important when it comes to personalised medicine. Artificial intelligence methods, such as machine learning, use this data to learn and help tailor diagnoses and therapies to individual needs in the future. However, such technology is still burdened with uncertainties. A team of researchers from Kaiserslautern and Leipzig is working on a system that automatically analyses and visualises medical data, including their uncertainties. The researchers will be presenting this technology at the medical technology trade fair “Medica” in Düsseldorf held from 13 to 16 November at the Rhineland-Palatinate research stand (stand 80, hall 3).

In the event of a stroke, speed is of essence. Using computer tomography (CT) scans, doctors can quickly determine the position of the blood clot in the brain and what treatment is appropriate. Such imaging procedures play an important role in medicine. They are also used in other areas, for example prior to operations. Magnetic resonance imaging (MRI) scans help surgeons plan an operation before it is performed.

What all these technologies have in common is that they generate a lot of data. “Analysing and visualising this data automatically is an important step toward personalised medicine,” says Dr. Christina Gillmann, a computer scientist at the University of Leipzig. “This area has gained enormous importance in recent years.” AI processes such as machine learning and neural networks make this possible. These networks learn on the basis of data with which they are trained or “fed”. They learn, for example, from CT image data a doctor has previously processed. In this way, technical information, but also medical experience, is incorporated. The rule is that the more data these methods can evaluate, the better the results will be.

In a few years, such technologies have the potential to be used in everyday clinical practice, for example, to enable personalised diagnoses and therapies. However, they are still in the early stages of development. “Each medical case has to be trained individually. The data must be prepared individually in advance, which is very time-consuming,” explains Robin Maack from the Computer Graphics and Human Computer Interaction working group at University Kaiserslautern-Landau as a problem. For each medical case, doctors have to “label” the data individually, for example. “This means that if a network is to train to automatically recognise a tumour, hundreds of images with known tumours have to be hand-drawn in so that the neural network has a basis with which to learn,” Gillmann explains.

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