How reliable is this algorithm at the moment?
Remmele: Right now, we achieve an accuracy of about 75 percent, meaning 75 percent of all image data sets are assigned to the correct class. Since the summer of 2017, another paper aims to further improve this rate, especially thanks to improved data preprocessing. Current research has many aspects that we can implement but we also have to invent many details because we are working with a very heterogeneous dataset.
What’s next for your project?
Remmele: We definitely have to adapt the methods to our problem to significantly improve the accuracy of our algorithm and make it suitable for everyday use. In addition, we have to identify whether we want to continue with a binary system of normal and abnormal findings or whether we want to indicate something akin to probabilities. I believe we could then considerably improve the process and save radiologists a lot of time if we could not only classify data but also point out where the algorithm discovered abnormalities in the dataset.
Needless to say, we also would like to implement the algorithm in the radiology information system. We are currently looking for industry partners to accomplish this. We also receive additional data from a General Hospital and a University Hospital. This will introduce increased heterogeneity into our existing dataset, designed to improve the effectiveness of training, among other things. The radiology office also continues to support us, which is quite remarkable because it does not have a research assignment like a University Hospital does. However, this research branch will urgently need commitment like this in the future. If we want to develop solutions for practicing physicians, we need their data.
How important is machine learning in medical technology today?
Remmele: Several manufacturers already provide solutions under the term "computer-aided diagnosis" that use machine learning in medical imaging as well as neurology. Screening tests for breast and lung cancer, for example, also apply tools that use classification algorithms in machine learning. Some products already tout image segmentation with deep learning. Today, these products are primarily used in specialized centers and university hospitals to monitor tumor progression for example.