Background Deep learning techniques can accurately detect and grade inflammatory findings on images from capsule endoscopy (CE) in Crohn's disease (CD). However, the predictive utility of deep learning of CE in CD for disease outcomes has not been examined. Objectives We aimed to develop a deep learning model that can predict the need for biological therapy based on complete CE videos of newly-diagnosed CD patients. Design This was a retrospective cohort study. The study cohort included treatment-naïve CD patients that have performed CE (SB3, Medtronic) within 6 months of diagnosis. Complete small bowel videos were extracted using the RAPID Reader software. Methods CE videos were scored using the Lewis score (LS). Clinical, endoscopic, and laboratory data were extracted from electronic medical records. Machine learning analysis was performed using the TimeSformer computer vision algorithm developed to capture spatiotemporal characteristics for video analysis. Results The patient cohort included 101 patients. The median duration of follow-up was 902 (354-1626) days. Biological therapy was initiated by 37 (36.6%) out of 101 patients. TimeSformer algorithm achieved training and testing accuracy of 82% and 81%, respectively, with an Area under the ROC Curve (AUC) of 0.86 to predict the need for biological therapy. In comparison, the AUC for LS was 0.70 and for fecal calprotectin 0.74. Conclusion Spatiotemporal analysis of complete CE videos of newly-diagnosed CD patients achieved accurate prediction of the need for biological therapy. The accuracy was superior to that of the human reader index or fecal calprotectin. Following future validation studies, this approach will allow for fast and accurate personalization of treatment decisions in CD.

中文翻译：

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小肠胶囊内窥镜视频的时空分析用于预测克罗恩病的结果。

背景 深度学习技术可以准确地检测克罗恩病 (CD) 胶囊内窥镜 (CE) 图像中的炎症表现并对其进行分级。然而，CE 深度学习对 CD 疾病结果的预测效用尚未得到检验。目标 我们的目标是开发一种深度学习模型，可以根据新诊断 CD 患者的完整 CE 视频来预测生物治疗的需要。设计 这是一项回顾性队列研究。研究队列包括诊断后 6 个月内进行过 CE（SB3，美敦力）的初次治疗 CD 患者。使用 RAPID Reader 软件提取完整的小肠视频。方法 使用 Lewis 评分 (LS) 对 CE 视频进行评分。从电子病历中提取临床、内窥镜和实验室数据。使用 TimeSformer 计算机视觉算法进行机器学习分析，该算法是为捕获视频分析的时空特征而开发的。结果 患者队列包括 101 名患者。中位随访时间为 902 (354-1626) 天。101 名患者中有 37 名 (36.6%) 开始了生物治疗。TimeSformer 算法的训练和测试准确率分别为 82% 和 81%，预测生物治疗需求的 ROC 曲线下面积 (AUC) 为 0.86。相比之下，LS 的 AUC 为 0.70，粪便钙卫蛋白的 AUC 为 0.74。结论 对新诊断 CD 患者的完整 CE 视频进行时空分析，可以准确预测是否需要生物治疗。其准确性优于人类读数指数或粪便钙卫蛋白。根据未来的验证研究，这种方法将允许快速、准确地个性化 CD 治疗决策。