Deep learning algorithms gained attention for detection (computer-aided detection [CADe]) of biliary tract cancer in digital single-operator cholangioscopy (dSOC). We developed a multimodal convolutional neural network (CNN) for detection (CADe), characterization and discriminating (computer-aided diagnosis [CADx]) between malignant, inflammatory, and normal biliary tissue in raw dSOC videos. In addition, clinical metadata were included in the CNN algorithm to overcome limitations of image-only models.

中文翻译：

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基于多模态卷积神经网络的算法，用于实时检测和区分胆管镜检查中恶性和炎性胆道狭窄：概念验证研究（附视频）


深度学习算法在数字单人胆管镜检查 （dSOC） 中检测（计算机辅助检测 [CADe]）胆道癌而受到关注。我们开发了一个多模态卷积神经网络 （CNN），用于检测 （CADe）、表征和区分（计算机辅助诊断 [CADx]）原始 dSOC 视频中的恶性、炎症性和正常胆道组织。此外，CNN 算法中还包含临床元数据，以克服仅图像模型的局限性。