  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.
