In the early 2000s, the introduction of single-camera wireless capsule endoscopy (CE) redefined small bowel study. Progress continued with the development of double-camera devices, first for the colon and rectum, and then, for panenteric assessment. Advancements continued with magnetic capsule endoscopy (MCE), particularly when assisted by a robotic arm, designed to enhance gastric evaluation. Indeed, as CE provides full visualization of the entire gastrointestinal (GI) tract, a minimally invasive capsule panendoscopy (CPE) could be a feasible alternative, despite its time-consuming nature and learning curve, assuming appropriate bowel cleansing has been carried out. Recent progress in artificial intelligence (AI), particularly in the development of convolutional neural networks (CNN) for CE auxiliary reading (detecting and diagnosing), may provide the missing link in fulfilling the goal of establishing the use of panendoscopy, although prospective studies are still needed to validate these models in actual clinical scenarios. Recent CE advancements will be discussed, focusing on the current evidence on CNN developments, and their real-life implementation potential and associated ethical challenges.
21世纪初，单摄像头无线胶囊内镜（CE）的问世重新定义了小肠检查方式。随着双摄像头设备的研发，该技术持续发展——最初应用于结肠与直肠检查，随后扩展至全肠道评估。磁控胶囊内镜（MCE）的出现进一步推动技术进步，尤其在机器人臂辅助下显著提升了胃部检查效果。事实上，由于胶囊内镜能实现全消化道可视化，在完成充分肠道准备的前提下，尽管存在耗时较长及学习曲线问题，微创式全消化道胶囊内镜（CPE）已成为可行的替代方案。人工智能（AI）领域的最新进展，特别是卷积神经网络（CNN）在胶囊内镜辅助读片（病灶检测与诊断）方面的开发，可能为实现全消化道内镜普及目标提供关键支持，但仍需前瞻性研究在实际临床场景中验证这些模型。本文将探讨胶囊内镜的最新进展，重点分析CNN发展的现有证据、实际应用潜力及相关伦理挑战。
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