Medical imaging generates a lot of data, for example during computer tomography. This data is important when it comes to personalized medicine.
Reviewed by Lily Ramsey, LLMNov 3 2023 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 visualizes medical data, including their uncertainties.
In a few years, such technologies have the potential to be used in everyday clinical practice, for example, to enable personalized 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.
Such uncertainties occur, for example, with lesions. During a stroke, certain areas of the brain are no longer supplied with sufficient oxygen, or not at all, due to the blockage of vessels in the brain. They are no longer able to work efficiently. The core of the lesion is often easy to recognize, but at the edge there is usually no clear demarcation and regions where even doctors cannot agree whether they should be classified as a lesion or not.
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