{"id":1821,"date":"2019-08-27T16:52:20","date_gmt":"2019-08-27T14:52:20","guid":{"rendered":"https:\/\/test.re-mic.de\/projects\/barrett\/"},"modified":"2023-08-02T16:16:59","modified_gmt":"2023-08-02T14:16:59","slug":"barrett","status":"publish","type":"page","link":"https:\/\/re-mic.de\/en\/projects\/barrett\/","title":{"rendered":"Barrett"},"content":{"rendered":"
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\n\t\t\t\t\t\tML4Gastro - AI projects for computer assistance in endoscopy of the upper gastrointestinal tract\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
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Motivation<\/h4>\n

Reflux is the increased backflow of acidic stomach contents into the esophagus. Chronic reflux is the main cause of the Barrett's esophagus, a lesion of the mucous membrane with an increased risk of developing esophageal cancer. The survival chances of affected patients are considered poor, as the disease is usually diagnosed at a late stage. If a standard drug treatment of reflux is not successful, an endoscopic examination may be indicated to detect treatable symptoms as early as possible. However, this is not unproblematic, because many reflux patients are endoscopically negative, i.e. mucous membrane lesions are not visible despite the presence of disease (low sensitivity of the examination). The significance in case of a pathological finding is relatively high (high specificity of the examination).<\/p>\n

Goals and procedure<\/h4>\n

Machine learning methods are increasingly being used in diagnostic imaging procedures. With the help of deep learning approaches, the physician should be supported in reliably detecting reflux-related mucous membrane damage, in particular (pre-)carcinogenic lesions, when evaluating endoscopic images. Based on the machine evaluation of endoscopic images, conclusions on the severity of a possible disease should be drawn.\u00a0Through the use of Deep Learning, a quality in the diagnostic evaluation of medical images has been achieved several times in recent years that not only reaches the medical \"gold standard\", but even exceeds it. This means that physician and computer meet at eye level, so that in the future, for example, the computer could at least be established as a second assessor.<\/p>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div><\/div><\/div><\/div>

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Cooperation partners<\/h3>\n