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Deep Learning in Gastrointestinal Endoscopy

机译：胃肠内窥镜检查深度学习

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Gastrointestinal (GI) endoscopy is used to inspect the lumen or interior of the GI tract for several purposes, including, (1) making a clinical diagnosis, in real time, based on the visual appearances; (2) taking targeted tissue samples for subsequent histopathological examination; and (3) in some cases, performing therapeutic interventions targeted at specific lesions. GI endoscopy is therefore predicated on the assumption that the operator-the endoscopist-is able to identify and characterize abnormalities or lesions accurately and reproducibly. However, as in other areas of clinical medicine, such as histopathology and radiology, many studies have documented marked interobserver and intraobserver variability in lesion recognition. Thus, there is a clear need and opportunity for techniques or methodologies that will enhance the quality of lesion recognition and diagnosis and improve the outcomes of GI endoscopy.
机译：胃肠道（GI）内窥镜检查用于检查GI道的内腔或内部，包括（1）实时基于视觉外观实时诊断; （2）服用靶向组织样品进行后续组织病理学检查; （3）在某些情况下，进行针对特定病变的治疗干预措施。 因此，在假设操作员 - 内窥镜师 - 能够准确且可重复地识别和表征异常或病变的假设上，GI内窥镜检查是预测的。 然而，如临床医学的其他领域，如组织病理学和放射学，许多研究都记录了病变识别的标记的interobserver和陷入腹部过度的变化。 因此，可以提高病变识别和诊断质量的技术或方法的清晰需求和机会，并改善GI内窥镜检查的结果。

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