{"id":4754,"date":"2022-01-17T17:43:00","date_gmt":"2022-01-17T16:43:00","guid":{"rendered":"https:\/\/re-mic.de\/?page_id=4754"},"modified":"2022-07-25T17:34:00","modified_gmt":"2022-07-25T15:34:00","slug":"celiac","status":"publish","type":"page","link":"https:\/\/re-mic.de\/en\/projects\/celiac\/","title":{"rendered":"Celiac"},"content":{"rendered":"
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\n\t\t\t\t\t\tML4Gastro - Celiac\t\t\t\t\t\t<\/h3>\n\t\t\t\t\t\t<\/div>\n<\/div><\/div><\/div><\/div>
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Project description<\/h3>\n

Motivation<\/h4>\n

Celiac disease is an autoimmune disease caused by gluten and leads to an inflammatory reaction of the small intestine. Due to its multifaceted nature and difficult to identify endoscopic appearance, celiac disease is often overlooked in routine endoscopies and is therefore underrepresented. Therefore, the aim of this project is to detect celiac disease in endoscopic images using deep learning techniques and thus provide guidance to the investigator.<\/p>\n

Goals and procedure<\/h4>\n

Nowadays, Machine Learning methods are increasingly used by physicians in clinical practice to assist them in diagnosing diseases. However, these methods usually use only one source of information for their decision (endoscopic images, CT scan, ...), whereas physicians base their diagnosis on a variety of information. Therefore, the aim of this project is to use additional sources of information (modalities) for decision making besides endoscopic images. As additional information, clinical parameters such as gender, Hb value or whether the patient suffers from abdominal complaints are available. This information must be processed appropriately and made available to the AI, which can then combine it to make a more accurate diagnosis. One challenge here is that not all the information has always been collected for a patient.<\/p>\n<\/div>\n<\/div><\/div><\/div><\/div>

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Cooperation partners<\/h3>\n