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Ähnlich verhält es sich, wenn unsere Immunzellen zwar aktiviert werden, es ihnen aber nicht gelingt, zum Krebsgeschwür vorzudringen. 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Dieses spannt eine Brücke zwischen neusten wissenschaftlichen und klinischen Erkenntnissen und erklärt zugleich, warum Krebsimmuntherapien bei jedem Patienten unterschiedlich wirken können. 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Und wir verstehen immer besser, welche Rolle dabei unser Immunsystem spielt. Diese Erkenntnisse bieten uns heute die einmalige Chance, personalisierte Krebsimmuntherapien zu entwickeln. 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The system then applies its knowledge to novel input data of the same type. In this case, we gave our computers a large set of CFP and OCT data from participants in two large DME clinical trials to train on.","type":"text"}]},{"type":"paragraph","content":[{"text":"The deep learning system examined a total of 17,997 CFP images from ~700 patients and compared them with corresponding OCT thickness measurements. The best model we developed using this training set was able to predict macular thickness greater than the 250 micron threshold with an accuracy of 97 percent — an impressive level of performance. 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When we looked into it, we were thrilled to find that the computer was focusing on the same parts of the images as specialists have been doing for years, such as the contours and calibre of blood vessels.","type":"text"}]},{"type":"paragraph","content":[{"text":"To test that finding, we still need to validate our system by testing it on other datasets. But presuming it works well, this tool could be of tremendous value to ophthalmologists as they treat patients with diabetes and DME. Once DME patients begin treatment, for example, many of them have to be seen every four weeks for OCT testing to ensure that their condition is not progressing. AI might enable people to use a cell phone camera to monitor their retinal tissue in real time, making it much easier for doctors to keep track of their patients’ need for and response to treatment. We could even envision an app to assess whether treatment is working. 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