Sorbonne Université, Centre d'Endoscopie Digestive, Hôpital Saint-Antoine, APHP, Paris, France.
ETIS UMR 8051 (CY Paris Cergy University, ENSEA, CNRS), Cergy, France.
J Gastroenterol Hepatol. 2021 Jan;36(1):12-19. doi: 10.1111/jgh.15341.
Neural network-based solutions are under development to alleviate physicians from the tedious task of small-bowel capsule endoscopy reviewing. Computer-assisted detection is a critical step, aiming to reduce reading times while maintaining accuracy. Weakly supervised solutions have shown promising results; however, video-level evaluations are scarce, and no prospective studies have been conducted yet. Automated characterization (in terms of diagnosis and pertinence) by supervised machine learning solutions is the next step. It relies on large, thoroughly labeled databases, for which preliminary "ground truth" definitions by experts are of tremendous importance. Other developments are under ways, to assist physicians in localizing anatomical landmarks and findings in the small bowel, in measuring lesions, and in rating bowel cleanliness. It is still questioned whether artificial intelligence will enter the market with proprietary, built-in or plug-in software, or with a universal cloud-based service, and how it will be accepted by physicians and patients.
基于神经网络的解决方案正在开发中，以减轻医生对小肠胶囊内镜检查的繁琐任务。计算机辅助检测是一个关键步骤，旨在减少阅读时间的同时保持准确性。弱监督解决方案已经显示出了有希望的结果；然而，视频级别的评估很少，并且还没有进行前瞻性研究。通过监督机器学习解决方案的自动特征化（在诊断和相关性方面）是下一步。它依赖于大型的、经过彻底标记的数据库，而专家的初步“真实”定义对此非常重要。其他的开发工作正在进行中，以帮助医生在小肠中定位解剖学标志和发现、测量病变和评估肠道清洁度。人工智能将以专有的、内置的或插件软件，还是以通用的基于云的服务进入市场，以及医生和患者将如何接受它，这些问题仍在讨论之中。
