Objective: To develop and validate a deep learning model for the identification of out-of-body images in endoscopic videos. Background: Surgical video analysis facilitates education and research. However, video recordings of endoscopic surgeries can contain privacy-sensitive information, especially if out-of-body scenes are recorded. Therefore, identification of out-of-body scenes in endoscopic videos is of major importance to preserve the privacy of patients and operating room staff. Methods: A deep learning model was trained and evaluated on an internal dataset of 12 different types of laparoscopic and robotic surgeries. External validation was performed on two independent multicentric test datasets of laparoscopic gastric bypass and cholecystectomy surgeries. All images extracted from the video datasets were annotated as inside or out-of-body. Model performance was evaluated compared to human ground truth annotations measuring the receiver operating characteristic area under the curve (ROC AUC). Results: The internal dataset consisting of 356,267 images from 48 videos and the two multicentric test datasets consisting of 54,385 and 58,349 images from 10 and 20 videos, respectively, were annotated. Compared to ground truth annotations, the model identified out-of-body images with 99.97% ROC AUC on the internal test dataset. Mean $\pm$ standard deviation ROC AUC on the multicentric gastric bypass dataset was 99.94$\pm$0.07% and 99.71$\pm$0.40% on the multicentric cholecystectomy dataset, respectively. Conclusion: The proposed deep learning model can reliably identify out-of-body images in endoscopic videos. The trained model is publicly shared. This facilitates privacy preservation in surgical video analysis.

翻译：目的：开发并验证一种用于识别内窥镜视频中体外图像的深度学习模型。背景：手术视频分析有助于教育和研究。然而，内窥镜手术的视频记录可能包含隐私敏感信息，尤其是在记录体外场景时。因此，识别内窥镜视频中的体外场景对于保护患者和手术室工作人员的隐私至关重要。方法：基于包含12种不同类型腹腔镜和机器人手术的内部数据集，训练并评估了一种深度学习模型。外部验证在两个独立的多中心测试数据集（腹腔镜胃旁路手术和胆囊切除术）上进行。从视频数据集中提取的所有图像均被标注为体内或体外。模型性能通过与人工基准标注进行比较来评估，测量受试者工作特征曲线下面积（ROC AUC）。结果：内部数据集包含来自48个视频的356,267张图像，两个多中心测试数据集分别包含来自10个和20个视频的54,385张和58,349张图像，均已完成标注。与基准标注相比，该模型在内部测试数据集上识别体外图像的ROC AUC达到99.97%。在多中心胃旁路数据集上，平均ROC AUC为99.94±0.07%；在多中心胆囊切除术数据集上，平均ROC AUC为99.71±0.40%。结论：所提出的深度学习模型能够可靠地识别内窥镜视频中的体外图像。该训练模型已公开共享，有助于在手术视频分析中保护隐私。