Since colonoscopy and polypectomy were introduced, Colorectal Cancer (CRC) incidence and mortality decreased significantly. Although we have entered the era of quality measurement and improvement, literature shows that a considerable amount of colorectal neoplasia is still missed by colonoscopists up to 25%, leading to an high rate of interval colorectal cancer that account for nearly 10% of all diagnosed CRC. Two main reasons have been recognised: recognition failure and mucosal exposure. For this purpose, Artificial Intelligence (AI) systems have been recently developed that identify a “hot” area during the endoscopic examination. In retrospective studies, where the systems are tested with a batch of unknown images, deep learning systems have shown very good performances, with high levels of accuracy. Of course, this setting may not reflect actual clinical practice where different pitfalls can occur, like suboptimal bowel preparation or poor examination technique. For this reason, a number of randomised clinical trials have recently been published where AI was tested in real time during endoscopic examinations. We present here an overview on recent literature addressing the performance of Computer Assisted Detection (CADe) of colorectal polyps in colonoscopy.

自从引入结肠镜检查和息肉切除术后，结直肠癌 (CRC) 的发病率和死亡率显着下降。虽然我们已经进入了质量测量和改进的时代，但文献显示，仍有高达 25% 的结肠镜检查者遗漏了相当多的结直肠肿瘤，导致间隔性结直肠癌的发病率很高，占所有确诊结直肠癌的近 10% . 已经认识到两个主要原因：识别失败和粘膜暴露。为此，最近开发了人工智能 (AI) 系统，可以在内窥镜检查期间识别“热”区域。在回顾性研究中，系统用一批未知图像进行测试，深度学习系统表现出非常好的性能，具有很高的准确性。当然，这种设置可能无法反映实际的临床实践，其中可能会出现不同的陷阱，例如肠道准备不理想或检查技术不佳。出于这个原因，最近发表了一些随机临床试验，其中在内窥镜检查期间实时测试了人工智能。我们在此概述了最近有关结肠镜检查中结直肠息肉计算机辅助检测 (CADe) 性能的文献。